@article {2436,
	title = {Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening},
	journal = {Frontiers in Immunology},
	volume = {17},
	year = {2026},
	doi = {https://doi.org/10.3389/fimmu.2026.1625837},
	author = {Sofie Aerts and Lorin Werthen-Brabants and Hamza Khan and Diana Giraldo and Edward De Brouwer and Lotte Geys and Veronica Popescu and Jan Sijbers and Henry C. Woodruff and Tom Dhaene and Dirk Deschrijver and Bart Van Wijmeersch and Philippe Lambin and Liesbet M. Peeters}
}
@article {2432,
	title = {Investigating the Influence of Individual Physical Effects on Dimensional Measurement Deviations Using XCT Simulations},
	journal = {Measurement,},
	volume = {263},
	year = {2026},
	doi = {https://doi.org/10.1016/j.measurement.2025.120168},
	author = {Miroslav Yosifov and Thiago Linhares Fernandes and Filippo Zanini and Simone Carmignato and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@article {2455,
	title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2507 - 2534},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30429},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30429https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30429},
	author = {Jelescu, Ileana O. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Schilling, Kurt G.}
}
@article {2456,
	title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2{\textemdash}Ex vivo imaging: Added value and acquisition},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2535 - 2560},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30435},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30435https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30435},
	author = {Schilling, Kurt G. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Howard, Amy F. D. and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@article {2457,
	title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3{\textemdash}Ex vivo imaging: Data processing, comparisons with microscopy, and tractography},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2561 - 2582},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30424},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30424},
	author = {Schilling, Kurt G. and Howard, Amy F. D. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@article {2383,
	title = {Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-024-01147-9},
	author = {Miroslav Yosifov and Thomas Lang and Virginia Florian and Stefan Gerth and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@article {2426,
	title = {Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Worsening in People with Multiple Sclerosis},
	journal = {Front. Neurosci.},
	volume = {19},
	year = {2025},
	doi = {https://doi.org/10.3389/fnins.2025.1610401},
	author = {Hamza Khan and Diana Giraldo and Henry C. Woodruff and Lorin Werthen-Brabants and Shruti Atul Mali and Sina Amirrajab and Edward De Brouwer and Veronica Popescu and Bart Van Wijmeersch and Oliver Gerlach and Jan Sijbers and Liesbet M. Peeters and Philippe Lambin}
}
@article {2399,
	title = {Loss of the Ubiquitin-Associated Domain of sqstm1/p62 in Zebrafish Causes a Phenotype Resembling Paget{\textquoteright}s Disease of Bone},
	journal = {Calcified Tissue International},
	volume = {116},
	year = {2025},
	pages = {1-15},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Dylan Bergen and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Lucia Vergauwen and Dorien Schepers and Evelien Van Dijck and Qiao Tong and Anja Verhulst and Jan De Beenhouwer and Jan Sijbers and Chrissy Hammond and Antonella Forlino and Geert Mortier and Paul Coucke and P Eckhard Witten and Ronald Young Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@article {2434,
	title = {Moisture Content Estimation of Porous Building Stones using Hyperspectral Imaging},
	journal = {Scientific Data},
	volume = {13},
	year = {2025},
	month = {Dec},
	pages = {102},
	abstract = {Moisture poses a major threat to built heritage through processes such as frost damage, salt crystallization and biological growth, which accelerate stone deterioration. Stone moisture content can be determined non-destructively by spectral reflectance in the shortwave infrared (SWIR) wavelength range. To validate this, large cubic and small cylindrical samples from six stone types, Brick, Euville, Massangis, Neubrunn, Obernkirchen, and Savonni{\`e}res, with different controlled moisture levels were prepared, and a comprehensive SWIR hyperspectral image dataset was created. The acquired hyperspectral images were processed using the Normalized Relative Arc Lengths (NRAL) method to generate moisture maps that illustrate the spatial distribution of water within each sample. Because moisture distribution is influenced by pore characteristics and grain arrangement, the moisture maps were validated through petrographic examination. For quantitative validation, the mean moisture content of each sample was compared with the corresponding gravimetric moisture content. The results demonstrated strong agreement between the estimated and gravimetric moisture values, with root mean square errors ranging from 1 and 2 g/g ~{\texttimes} 100.},
	issn = {2052-4463},
	doi = {10.1038/s41597-025-06416-4},
	url = {https://doi.org/10.1038/s41597-025-06416-4},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Cristina, Laura and De Kock, Tim and Fontaine, Laurent and Hayen, Roald and Paul Scheunders}
}
@article {2422,
	title = {Soil Moisture Content Estimation From Hyperspectral Remote Sensing Data},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {18},
	year = {2025},
	pages = {22231-22240},
	abstract = {Because of its significant absorption power, especially in the shortwave infrared optical region, water dominates the optical reflectance properties of water-bearing materials. This allows us to study a material{\textquoteright}s water-related features, such as its moisture content, from optical reflectance. In this study, we proposed a framework to estimate soil moisture content from PRISMA hyperspectral remote sensing data. The proposed framework requires a dry endmember spectrum and an endmember spectrum of high soil moisture along with ground truth moisture content, obtained from ground measurements. The method takes into account the complex interaction of light with soils, the large variation of environmental conditions, leading to spectral variability and the soil-specific behavior of water. The framework is extensively validated using ground-measured soil moisture data from the International Soil Moisture Network database. A total of 1418 PRISMA images corresponding to 151 ground stations were analyzed. From 518 retained images, a total root-mean-squared error of 8.682 \% and R2 of 0.385 was obtained.},
	keywords = {Absorption, Hyperspectral, Hyperspectral imaging, Land surface, Machine learning regression, Optical imaging, Optical reflection, Optical sensors, PRISMA, Reflectivity, remote sensing, Soil measurements, Soil moisture, soil moisture content (SMC), vegetation mapping},
	doi = {10.1109/JSTARS.2025.3603841},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2421,
	title = {A Spectral-Spatial Attention Network for Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {63},
	year = {2025},
	pages = {1-15},
	abstract = {Hyperspectral unmixing, an essential and fundamental task in remote sensing, focuses on estimating endmembers (spectrally pure components) and their fractional abundances within each mixed pixel of a hyperspectral image. With the advent of deep learning (DL), the field of hyperspectral unmixing has made significant progress. Among DL approaches, autoencoder-based models have shown promising results. However, most unmixing methods estimate the endmembers by the weights of the linear layers in the decoder of their networks, making their performance highly dependent on weight initialization. Moreover, noise is not explicitly accounted for in most recent methods that use spectral angle distance (SAD) loss. To avoid the initialization problems, we developed an innovative inversion strategy to directly estimate the endmembers. Moreover, to optimally account for noise, an end-to-end network is proposed, which integrates both denoising and unmixing. Finally, for an improved feature extraction, a novel spectral-spatial attention module (SSAM) is integrated into the network. Extensive experiments on synthetic and three real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods. The full code is available at https://github.com/xuanwentao for public evaluation.},
	keywords = {Attention module, autoencoder, Autoencoders, autonomous unmixing module (AUM), Convolutional neural networks, Data mining, Decoding, deep denoising module (DDM), Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Loss measurement, Neural networks, Noise reduction},
	doi = {10.1109/TGRS.2025.3576479},
	author = {Tao, Xuanwen and Koirala, Bikram and Rasti, Behnood and Plaza, Antonio and Paul Scheunders}
}
@article {2425,
	title = {Superimposing Synthetic Defects into Real XCT Data and Segmentation-Based Comparison for Advanced Probability of Detection Evaluation},
	journal = { Journal of Nondestructive Evaluation },
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-025-01262-1},
	author = {Miroslav Yosifov and Bernhard Fr{\"o}hler and Jan Sijbers and Jan De Beenhouwer and Johann Kastner and Christoph Heinzl}
}
@article {2370,
	title = {Determination of volumetric abundance of intimate mixture using Bayesian MCMC},
	journal = {IEEE Sensors Journal},
	year = {2024},
	pages = {1-1},
	keywords = {Grain size, Hyperspectral, intimate mixture, Iron, Minerals, Powders, radiative transfer, Reflectivity, Sensors, Uncertainty},
	doi = {10.1109/JSEN.2024.3463401},
	author = {Schmidt, Fr{\'e}d{\'e}ric and Koirala, Bikram and Andrieu, Fran{\c c}ois}
}
@inproceedings {2376,
	title = {Dynamic flat field correction in edge illumination imaging},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3028319},
	author = {Didem G{\"o}kbel Kekliko{\u g}lu and Nicholas Francken and Ben Huyge and Ziya Merdan and Jan De Beenhouwer and Jan Sijbers}
}
@article {2362,
	title = {An efficient method for water content estimation of building materials from spectral reflectance},
	journal = {NDT \& E International},
	volume = {147},
	year = {2024},
	pages = {103214},
	abstract = {We propose a nondestructive methodology to accurately estimate the water content of building materials from spectral reflectance in the shortwave infrared. The water content of a wet sample is estimated from the relative position of its reflectance spectrum on the curve between 2 reference spectra with known water content. By design, the approach is invariant to variations in illumination and acquisition conditions. Validation is done on datasets of clay powders and bricks. The experimental results provide confirmation of the effectiveness of the proposed method.},
	keywords = {Brick, Clay, Cultural heritage, Hyperspectral, remote sensing, Water content},
	issn = {0963-8695},
	doi = {https://doi.org/10.1016/j.ndteint.2024.103214},
	url = {https://www.sciencedirect.com/science/article/pii/S0963869524001798},
	author = {Bikram Koirala and Paul Scheunders}
}
@inproceedings {2398,
	title = {Exploring the Potential of Hyperspectral Imaging to Estimate the Moisture Content in Natural and Historical Stones},
	booktitle = {2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2024},
	pages = {1-5},
	keywords = {Building Physics, Cultural differences, Damage Monitoring, Estimation, Hyperspectral imaging, Moisture, Moisture content, Monitoring, Protocols, Reflectivity, Reliability, Signal processing, Water conservation},
	doi = {10.1109/WHISPERS65427.2024.10876512},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Cristina, Laura and Hayen, Roald and Paul Scheunders}
}
@inproceedings {2397,
	title = {Improving Spectral Unmixing Performance by Frequency Component Reduction},
	booktitle = {2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2024},
	pages = {1-5},
	keywords = {Fast Fourier Transform, Fast Fourier transforms, Genetic algorithms, Hyperspectral, Hyperspectral imaging, intimate mixtures, Noise, Noise level, Redundancy, Robustness, Signal processing algorithms, Stability analysis, Surface treatment, unmixing},
	doi = {10.1109/WHISPERS65427.2024.10876530},
	author = {Bnoulkacem, Zakaria and Koirala, Bikram and Paul Scheunders}
}
@article {2382,
	title = {A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2024},
	pages = {1-1},
	keywords = {Accuracy, dual-feature fusion, Estimation, Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Noise reduction, Reflectivity, Scattering, similarity, Transformers, Transforms, unmixing module},
	doi = {10.1109/TGRS.2024.3505292},
	author = {Tao, Xuanwen and Koirala, Bikram and Plaza, Antonio and Paul Scheunders}
}
@article {2361,
	title = {Nonlinear Spectral Unmixing Using B{\'e}zier Surfaces},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {62},
	year = {2024},
	pages = {1-16},
	keywords = {B{\'e}zier surface, Data models, Hyperspectral, Hyperspectral imaging, Manifolds, mineral powder mixtures, mixing models, nonlinearity, Reflectivity, Sensor phenomena and characterization, spectral variability, Surface treatment, Training},
	doi = {10.1109/TGRS.2024.3422495},
	author = {Koirala, Bikram and Rasti, Behnood and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2364,
	title = {Perceptual super-resolution in multiple sclerosis MRI},
	journal = {Frontiers in Neuroscience},
	volume = {18},
	year = {2024},
	doi = {10.3389/fnins.2024.1473132},
	author = {Diana Giraldo and Hamza Khan and Gustavo Pineda and Liang, Zhihua and Alfonso Lozano and Bart Van Mijweersch and Henry C. Woodruff and Philippe Lambin and Romero, Eduardo and Liesbet M. Peeters and Jan Sijbers}
}
@article {2344,
	title = {A Supervised Approach for Estimating Fractional Abundances of Binary Intimate Mixtures},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2024},
	pages = {1-11},
	keywords = {Earth, Estimation, Hyperspectral, Hyperspectral imaging, Mathematical models, mineral powder mixtures, mixing models, nonlinearity, Powders, Reflectivity, spectral variability, Vectors},
	doi = {10.1109/JSTARS.2024.3387750},
	author = {Koirala, Bikram and Rasti, Behnood and Paul Scheunders}
}
@article {2355,
	title = {Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study},
	journal = {Journal of Experimental Orthopaedics},
	volume = {11},
	year = {2024},
	pages = {e12090},
	doi = {https://doi.org/10.1002/jeo2.12090},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Kristien Vuylsteke and Jan Sijbers and Lotem Mahluf and Peter Angele and Elizaveta Kon and Peter Verdonk}
}
@conference {2394,
	title = {Validation of abundance determination of granular mixture using radiative transfer and Bayesian MCMC},
	year = {2024},
	abstract = {We used the semi-analytical reflectance model from (Hapke, 2012), which is a good compromise between physical realism and efficient computation time. In Andrieu et al., 2022, we demonstrated that the usual gradient-descent method is not helpful because the non-linearities are so strong that the results mainly depend on the initialization. So the analysis is done by a Monte Carlo Bayesian approach to propagate the uncertainties from the measurement in reflectance to the parameters (Cruz Mermy et al., 2023). This operation is sometimes called inversion or assimilation.},
	url = {https://meetingorganizer.copernicus.org/EPSC2024/EPSC2024-902.html},
	author = {Fr{\'e}d{\'e}ric Schmidt and Bikram Koirala and Fran{\c c}ois Andrieu}
}
@inproceedings {2333,
	title = {Acritical Comparison of Linear and Nonlinear Unmixing for Intimate Mixtures},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Conferences, Estimation, hyperspectral image, Hyperspectral imaging, intimate mixtures, linear and nonlinear unmixing, Powders, Reflectivity, Signal processing, Spectroradiometers},
	doi = {10.1109/WHISPERS61460.2023.10431354},
	author = {Koirala, Bikram and Das, Samiran and Rasti, Behnood and Ghamisi, Pedram and Gloaguen, Richard and Paul Scheunders}
}
@article {2256,
	title = {ADEPT: Accurate Diffusion EPI with multi-contrast shoTs},
	journal = {Magnetic Resonance in Medicine},
	volume = {89},
	year = {2023},
	pages = {396-410},
	doi = {10.1002/mrm.29398},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Stefan Klein and Johan Van Audekerke and Verhoye, Marleen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2335,
	title = {Corrosion detection using hyperspectral VNIR and SWIR imaging for drone-based inspection of electrical pylons},
	booktitle = {EUROCORR 2023, European Corrosion Congress, Proceedings},
	year = {2023},
	pages = {2},
	author = {Bnoulkacem, Zakaria and Vlaminck, Michiel and Luzardo Morocho, Gonzalo Raimundo and Zivariadab, Hamed and Zahiri, Zohreh and Koirala, Bikram and Platisa, Ljiljana and Luong, Hiep and Paul Scheunders and Mangialetto, Fr{\'e}d{\'e}ric and Bufi, Irid}
}
@inproceedings {2293,
	title = {Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934 },
	doi = {doi.org/10.58286/27716},
	author = {Miroslav Yosifov and Patrick Weinberger and Michael Reiter and Bernhard Fr{\"o}hler and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2334,
	title = {Drone-Based Corrosion Detection on High-Voltage Transmission Towers Using Hyperspectral Imaging},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Corrosion, corrosion detection, Drones, High-voltage techniques, high-voltage tower, Hyperspectral imaging, Inspection, Pipelines, Poles and towers, UAV},
	doi = {10.1109/WHISPERS61460.2023.10431111},
	author = {Vlaminck, Michiel and Bnoulkacem, Zakaria and Luzardo, Gonzalo and Zivariadab, Hamed and Zahiri, Zohreh and Koirala, Bikram and Mangialetto, Fr{\'e}d{\'e}ric and Bufi, Irid and Platisa, Ljiljana and Jayapala, Murali and Paul Scheunders and Luong, Hiep}
}
@article {2297,
	title = {dtiRIM: A generalisable deep learning method for diffusion tensor imaging},
	journal = {Neuroimage},
	volume = {269},
	year = {2023},
	chapter = {119900},
	doi = {10.1016/j.neuroimage.2023.119900},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Ben Jeurissen and Dirk H J Poot}
}
@conference {2300,
	title = {Exploring the Correlation between Disability Status and Brain Volumetric Measurements Using Real-World Retrospective Magnetic Resonance Images in People with Multiple Sclerosis},
	number = {1637},
	year = {2023},
	address = {October 11-13, Milan, Italy},
	abstract = {This study investigated whether retrospective real-world MR image databases can be used to obtain MR-based biomarkers of multiple sclerosis (MS) disability by examining correlations between Expanded Disability Status Scale (EDSS) scores and volumetric measurements from reconstructed T2-weighted fluid-attenuated inversion recovery (FLAIR) in people with MS (PwMS).},
	author = {Hamza Khan and Diana Giraldo and Quinten Beirinckx and Jan Sijbers and Philippe Lambin and Henry C. Woodruff and Liesbet M. Peeters}
}
@inproceedings {2317,
	title = {An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders},
	booktitle = { IEEE International Geoscience and Remote Sensing Symposium},
	year = {2023},
	month = {20 October 2023},
	pages = {5890-5893},
	abstract = {Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain.In this work, we generated a comprehensive hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets)},
	doi = {10.1109/IGARSS52108.2023.10281467},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2324,
	title = {A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures},
	journal = {IEEE Sensors Journal},
	year = {2023},
	month = {2023/12/28},
	abstract = {Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. 

In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets.},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Sandra Lorenz and Margret Fuchs and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@inproceedings {2331,
	title = {Soil Moisture Content Estimation from Hyperspectral Remote Sensing Data},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Hyperion EO-1, Hyperspectral, Hyperspectral imaging, machine learning, Moisture, Optical reflection, Optical saturation, Optical sensors, Optical signal processing, remote sensing, Soil moisture, soil moisture content},
	doi = {10.1109/WHISPERS61460.2023.10431194},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2318,
	title = {Study on the Potential of Oil Spill Monitoring in a Port Environment Using Optical Reflectance},
	journal = {Remote Sensing},
	year = {2023},
	month = {2023/10/13},
	abstract = {In this work, we studied the potential of the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed for accurate oil thickness and volume estimation using optical reflectance. The developed method was made invariant to changes in acquisition and illumination conditions. In the next step, an algorithm based on an artificial neural network was designed to detect spilled oil. The training samples that are required to optimize the parameters of the network were generated by utilizing the proposed physical model. To validate the method, experiments were conducted in laboratory and outdoor scenarios for detection and thickness/volume estimation on four different oil types. In particular, we developed hyperspectral datasets of oil samples with varying thickness between 500 {\textmu}m and 5000 {\textmu}m acquired using two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared hyperspectral camera, in strictly controlled experimental settings. To demonstrate the potential of the proposed method in outdoor environments using solely the visible wavelength region, we monitored the evolution of artificially spilled oil in an outdoor scene with an RGB camera mounted on a drone.},
	doi = {10.3390/rs15204950},
	author = {Bikram Koirala and Nicholus Mboga and Robrecht Moelans and Els Knaeps and Seppe Sels and Frederik Winters and Svetlana Samsonova and Steve Vanlanduit and Paul Scheunders}
}
@article {2289,
	title = {Systematic review of reconstruction techniques for accelerated quantitative MRI},
	journal = {Magnetic Resonance in Medicine},
	volume = {90},
	year = {2023},
	pages = {1172-1208},
	doi = {https://doi.org/10.1002/mrm.29721},
	author = {Banafshe Shafieizargar and Riwaj Byanju and Jan Sijbers and Stefan Klein and Arnold Jan den Dekker and Dirk H J Poot}
}
@inproceedings {2332,
	title = {Unmixing Intimate Mixtures Using B{\'e}zier Surfaces},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {hyperspectral image, Hyperspectral imaging, intimate mixtures, Lighting, nonlinear unmixing, Reflectivity, Signal processing, Surface reconstruction, Surface treatment, Training},
	doi = {10.1109/WHISPERS61460.2023.10431306},
	author = {Koirala, Bikram and Rasti, Behnood and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2286,
	title = {Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study},
	journal = {Journal of Neurotrauma},
	volume = {40},
	year = {2023},
	pages = {1317-1338},
	doi = {https://doi.org/10.1089/neu.2022.0365},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Evgenios N. Kornaropoulos and Sophie Richter and Roberto Paolella and Marta M. Correia and Ben Glocker and Guy Williams and Anne Vik and Jussi Posti and Asta Kristine H{\r a}berg and Jonas Stenberg and Pieter-Jan Guns and Arnold Jan den Dekker and David K. Menon and Jan Sijbers and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@inproceedings {2315,
	title = {Blind Nonlinear Unmixing For Intimate Mixtures Using Hapke Model And CNN},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2022},
	month = {22 November 2022},
	pages = {1-5},
	abstract = {We propose a blind nonlinear unmixing technique for intimate mixtures. We use the Hapke model and a fully convolutional neural networks (HapkeCNN). The proposed loss function contains 1) A quadratic term based on the Hapke model, 2) reconstruction error, and 3) a minimum volume term. The first term captures the nonlinearity, the second ensures the fidelity of the reconstructed reflectance, and the latter term exploits the geometrical information. The proposed method is evaluated using a simulated and a real datasets. We compare the results of endmember and abundance estimation with bilinear, multilinear, nonlinear, and projection-based linear unmixing techniques. The experimental results confirm that HapkeCNN considerably outperforms the state-of-the-art nonlinear approaches in terms of spectral angle distance and root mean square error. HapkeCNN was implemented in Python (3.9) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/HapkeCNN.},
	doi = {10.1109/WHISPERS56178.2022.9955081},
	author = {Behnood Rasti and Bikram Koirala}
}
@inproceedings {2311,
	title = {Deep Blind Unmixing using Minimum Simplex Convolutional Network},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {28-31},
	abstract = {This paper proposes a deep blind hyperspectral unmixing network for datasets without pure pixels called minimum simplex convolutional network (MiSiCNet). MiSiCNet is the first deep learning-based blind unmixing method proposed in the literature which incorporates both spatial and geometrical information of the hyperspectral data, in addition to the spectral information. The proposed convolutional encoder-decoder architecture incorporates the spatial information using convolutional filters and implicitly applying a prior on the abundances. We added a minimum simplex volume penalty term to the loss function to exploit the geometrical information. We evaluate the performance of MiSiCNet on simulated and real datasets. The experimental results confirm the robustness of the proposed method to both noise and absence of pure pixels. Additionally, MiSiCNet considerably outperforms the state-of-the-art unmixing approaches. The results are given in terms of spectral angle distance in degree for the endmember estimation, and root mean square error in percentage for the abundance estimation. MiS-iCNet was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/MiSiCNet.},
	doi = {10.1109/IGARSS46834.2022.9883117},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Jocelyn Chanussot}
}
@conference {2463,
	title = {dtiRIM: A recurrent inference machine for diffusion tensor estimation},
	year = {2022},
	pages = {1981},
	author = {Sabidussi, E R and Klein, S and Ben Jeurissen and Poot, D H J}
}
@article {2261,
	title = {HapkeCNN: Blind nonlinear unmixing for intimate mixtures using Hapke model and convolutional neural network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2022},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders}
}
@article {2410,
	title = {HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	pages = {1-15},
	keywords = {Albedo, Blind unmixing, convolutional neural network, deep learning (DL), endmember estimation, Estimation, Fans, Hapke model, hyperspectral image, image reconstruction, Kernel, minimum simplex volume, nonlinear unmixing, Photonics, Reflectivity, Solid modeling},
	doi = {10.1109/TGRS.2022.3202490},
	author = {Rasti, Behnood and Koirala, Bikram and Paul Scheunders}
}
@article {2313,
	title = {Hyperspectral Unmixing Using Transformer Network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	month = { 03 August 2022},
	pages = {1-16},
	abstract = {Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU .},
	doi = {10.1109/TGRS.2022.3196057},
	author = {Preetam Ghosh and Swalpa Kumar Roy and Bikram Koirala and Behnood Rasti and Paul Scheunders}
}
@article {2234,
	title = {Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images},
	journal = {Computers and Electronics in Agriculture},
	volume = {197},
	year = {2022},
	pages = {1-14},
	doi = {https://doi.org/10.1016/j.compag.2022.106962},
	author = {Tim Van De Looverbosch and Jiaqi, He and Astrid Tempelaere and Klaas Kelchtermans and Pieter Verboven and Tinne Tuytelaars and Jan Sijbers and Bart Nicolai}
}
@article {2220,
	title = {MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	doi = {https://doi.org/10.1109/TGRS.2022.3146904},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Jocelyn Chanussot}
}
@article {2408,
	title = {MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	pages = {1-15},
	keywords = {Blind unmixing, convolutional neural network, Data mining, deep learning, deep prior, endmember extraction, Estimation, hyperspectral image, Hyperspectral imaging, Indexes, Libraries, Minimization, minimum simplex volume, Optimization, unmixing},
	doi = {10.1109/TGRS.2022.3146904},
	author = {Rasti, Behnood and Koirala, Bikram and Paul Scheunders and Chanussot, Jocelyn}
}
@article {2221,
	title = {Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	doi = {10.1016/j.compag.2022.106806},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Bikram Koirala and Paul Scheunders}
}
@article {2246,
	title = {Probability of Detection applied to X-ray inspection using numerical simulations},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	pages = {536-551},
	doi = {10.1080/10589759.2022.2071892},
	author = {Miroslav Yosifov and M. Reiter and S. Heupl and C. Gusenbauer and Bernhard Fr{\"o}hler and R. Fernandez- Gutierrez and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@article {2316,
	title = {A Robust Supervised Method for Estimating Soil Moisture Content From Spectral Reflectance},
	journal = {IEEE Transactions on Geoscience and Remote Sensing },
	volume = {60},
	year = {2022},
	month = {05 October 2022},
	pages = {1-13},
	abstract = {Due to the complex interaction of light with moist soils, the soil moisture content (SMC) is hard to estimate from the soil spectral reflectance. Spectral variability, caused by variations in viewing and illumination angle and between-sensor variability, further complicates the estimation. In this work, we developed a supervised methodology to accurately estimate SMC from spectral reflectance. The method determines a proxy for the SMC of moist soil, making use of the reflectance spectra of an air-dried and saturated soil sample. The proxy is made invariant to illumination and viewing angle, and sensor type. In the next step, the proxy is normalized with respect to the ground-truth SMC of the saturated soil to make the technique less dependent on the soil type. The normalized proxy can be directly used as an estimate of SMC. Alternatively, the nonlinear relationship between the normalized proxy and the actual SMC can be learned by supervised regression. Experiments are conducted on real moist soil data. In particular, we developed datasets of moist minerals, acquired by two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared (SWIR) hyperspectral camera, under strictly controlled experimental settings. The proposed methodology is also validated on the available real moist soil data from the literature. Compared to state-of-the-art methods, the proposed method accurately estimates the SMC.},
	doi = {10.1109/TGRS.2022.3212600},
	author = {Bikram Koirala and Zohreh Zahiri and Paul Scheunders}
}
@inproceedings {2312,
	title = {A Robust Supervised Method to Estimate Chlorophyll Ab Content from Spectral Reflectance},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {326-329},
	abstract = {Leaf chlorophyll ab content is an important indicator of vegetation physiological status and is generally obtained from spectral reflectance. For non-destructive estimation of chlorophyll ab content, physical leaf reflectance models, such as the PROSPECT model and supervised methods have been applied. While the former generally does not perform optimal, the latter only performs well when trained on similar data. In this work, we developed a robust supervised method that overcomes this problem. The method derives a proxy for chlorophyll ab content as the relative position of a leaf reflectance spectrum on the arc spanned by the two extremes, containing high and low chlorophyll ab content. This proxy is found to be unaffected by spectral variability, caused by environmental and acquisition conditions. The relation between this proxy and the actual chlorophyll ab content is obtained by a supervised regression model, that is trained on a single leaf reflectance dataset, and that is transferable to other datasets. The proposed method is validated on seven real hyperspectral datasets.},
	doi = {10.1109/IGARSS46834.2022.9883839},
	author = {Bikram Koirala and Paul Scheunders}
}
@inproceedings {2320,
	title = {Sparse Unmixing using Deep Convolutional Networks},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {24-27},
	abstract = {This paper proposes a sparse unmixing technique using a convolutional neural network (SUnCNN). We reformulate the sparse unmixing problem into an optimization over the parameters of a convolutional network. Relying on a spectral library, the deep network learns in an unsuper-vised manner a mapping from a fixed input to the sparse abundances. Moreover, SUnCNN fulfills the sum-to-one constraint using a softmax activation layer. We compare SUnCNN with the state-of-the-art using a simulated and a real dataset. The experimental results show that the proposed deep learning-based unmixing method outperforms the oth-ers in terms of signal to reconstruction error. Additionally, SUnCNN is visually superior to the competing techniques. SUnCNN was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/SUnCNN.},
	doi = {10.1109/IGARSS46834.2022.9884790},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders}
}
@article {2249,
	title = {The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget{\textquoteright}s disease of bone?},
	journal = {Bone Reports},
	volume = {16},
	year = {2022},
	pages = {75-76},
	doi = {10.1016/j.bonr.2022.101483},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Dorien Schepers and Jan De Beenhouwer and Jan Sijbers and Antonella Forlino and Paul Coucke and P. Eckhard Witten and Ronald Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@conference {2170,
	title = {3D atomic resolution tomography from iDPC-STEM images using multiple atom model prior},
	year = {2021},
	author = {Shiva Hosseinnejad and E. G. T. Bosch and H. Kohr and I. Lazi{\'c} and V. Zharinov and E. Franken and Jan Sijbers and Jan De Beenhouwer}
}
@article {2143,
	title = {Associations between different white matter properties and reward-based performance modulation},
	journal = {Brain Structure and Function},
	year = {2021},
	month = {Apr-02-2021},
	issn = {1863-2653},
	doi = {10.1007/s00429-021-02222-x},
	url = {https://link.springer.com/content/pdf/10.1007/s00429-021-02222-x.pdf},
	author = {Park, Haeme R. P. and Verhelst, Helena and Quak, Michel and Ben Jeurissen and Krebs, Ruth M.}
}
@inproceedings {2307,
	title = {BOOSTING HYPERSPECTRAL IMAGE UNMIXING USING DENOISING: FOUR SCENARIOS},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	month = {12 October 2021},
	abstract = {We present an analysis of the influence of noise on the unmixing of hyperspectral data. We propose four scenarios to 1) investigate the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing and 2) study the relation between noise and different endmembers selection strategies. Experiments are conducted on a simu-1ated and a real datasets with a wide range of signal to noise ratios (from 10 to 50 dB).},
	doi = {10.1109/IGARSS47720.2021.9553942},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi and Richard Gloaguen}
}
@article {2137,
	title = {Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline{\textquoteright}s scale layer thickness},
	journal = {Alexandria Engineering Journal},
	volume = {60},
	year = {2021},
	abstract = {The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline{\textquoteright}s scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline{\textquoteright}s scale layer thickness.},
	author = {Mohammadmehdi Roshani and Giang T.T. Phan and Gholam Hossein Roshani and Robert Hanus and Trung Duong and Enrico Corniani and Ehsan Nazemi and ElMostafa Kalmouni}
}
@article {2206,
	title = {Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition},
	journal = {Remote Sensing},
	volume = {13},
	year = {2021},
	abstract = {Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.},
	doi = {https://doi.org/10.3390/rs13152930},
	author = {M. Zare and M. Sadegh Helfroush and K. Kazemi and Paul Scheunders}
}
@conference {2174,
	title = {Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study},
	year = {2021},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@conference {2185,
	title = {Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI},
	volume = {34},
	year = {2021},
	pages = {S54},
	doi = {10.1007/s10334-021-00947-8},
	author = {Maira Siqueira Pinto and Stefan Winzeck and S. Richter and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Pieter-Jan Guns and Arnold Jan den Dekker and Jan Sijbers and Virginia F. J. Newcombe and Pieter Van Dyck}
}
@article {2095,
	title = {Outlier detection for foot complaint diagnosis: modeling confounding factors using metric learning},
	journal = {IEEE Intelligent Systems},
	volume = {36},
	year = {2021},
	pages = {41-49},
	doi = {10.1109/MIS.2020.3046431},
	author = {Brian G. Booth and No{\"e}l L.W. Keijsers and Jan Sijbers}
}
@article {2190,
	title = {Recurrent Inference Machines as inverse problem solvers for MR relaxometry},
	journal = {Medical Image Analysis},
	volume = {74},
	year = {2021},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.media.2021.102220},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@article {2086,
	title = {Robust supervised method for nonlinear spectral unmixing accounting for endmember variability},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {59},
	year = {2021},
	pages = {7434-7448},
	abstract = {Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinearly related to the pure material endmember spectra, making it hard to estimate the fractional abundances of the materials. Changing illumination conditions and cross-sensor situations cause spectral variability, further complicating the unmixing procedure. In this work, we propose a supervised approach to unmix mineral powder mixtures, containing endmember variability.

First, the abundances are estimated by calculating the geodesic distances between the mixtures and the endmembers. It is argued and experimentally validated that the estimated geodesic abundances, although not correct, are invariant to external spectral variability.  Then, a supervised approach is applied to learn a mapping from the obtained geodesic abundances to spectra that follow a linear model. To learn this mapping, ground truth fractional abundances of a number of training samples are required. 
Although any nonlinear regression method can be used to learn the mapping, Gaussian process is found to be suitable when a limited number of training samples are available. The trained model is applicable to all manifolds that contain a similar nonlinear behavior as the trained manifold, e.g. when the same mixtures are measured by another sensor. Using the output spectra, a simple inversion of the linear model reveals the true abundances.

Experiments are conducted on simulated and real mineral mixtures. In particular, we developed data sets of homogeneously mixed mineral powder mixtures, acquired by 2 different sensors, an Agrispec spectrometer and a snapscan shortwave infrared hyperspectral camera, under strictly controlled experimental settings. The proposed approach is compared to other supervised approaches and nonlinear mixture models.  },
	doi = {https://doi.org/10.1109/TGRS.2020.3031012},
	author = {Bikram Koirala and Zohreh Zahiri and Alfredo Lamberti and Paul Scheunders}
}
@inproceedings {2254,
	title = {Spectral Unmixing Using Deep Convolutional Encoder-Decoder},
	booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	month = {12 October 2021},
	pages = {3829-3832},
	abstract = {In this paper, we introduce {\textquoteleft}Unmixing Deep Image Prior{\textquoteright} (UnDIP), a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e. a simplex volume maximization in a subspace of the dataset. Then, the abundances are estimated using a deep image prior. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral dataset. The results show considerable improvements compared to state-of-the-art methods.},
	doi = {10.1109/IGARSS47720.2021.9553425},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@article {2308,
	title = {SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {19},
	year = {2021},
	month = {06 August 2021},
	pages = {1-5},
	abstract = {In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing. It uses a deep convolutional encoder{\textendash}decoder to generate the abundances relying on a spectral library. We reformulate the sparse unmixing into an optimization over the deep network{\textquoteright}s parameters. Therefore, the deep network learns in an unsupervised manner to map a fixed input into the sparse optimum abundances. Additionally, SUnCNN holds the sum-to-one constraint using a softmax activation layer. The proposed method is compared with the state-of-the-art using two synthetic datasets and one real hyperspectral dataset. The overall results confirm that the proposed method outperforms the other ones in terms of signal to reconstruction error (SRE). Additionally, SUnCNN shows visual superiority for both real and synthetic datasets compared with the competing techniques. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/SUnCNN .},
	doi = {10.1109/LGRS.2021.3100992},
	author = {Behnood Rasti and Bikram Koirala}
}
@article {2167,
	title = {UnDIP: hyperspectral unmixing using deep image prior},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2021},
	month = {31 March 2021},
	pages = {1-15},
	abstract = {In this article, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e., a simplex volume maximization in the subspace of the data set. Then, the abundances are estimated using a deep image prior. The main motivation of this work is to boost the abundance estimation and make the unmixing problem robust to noise. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral data set. The proposed method is evaluated on simulated and three real remote sensing data for a range of SNR values (i.e., from 20 to 50 dB). The results show considerable improvements compared to state-of-the-art methods. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/UnDIP.},
	doi = {10.1109/TGRS.2021.3067802},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@article {2057,
	title = {Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibres},
	journal = {Nondestructive Testing and Evaluation},
	volume = {35},
	year = {2020},
	pages = {328-341},
	doi = {https://doi.org/10.1080/10589759.2020.1774583},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Hans-Christian Hege and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2084,
	title = {Aperture size selection for improved brain tumor detection and quantification in multi-pinhole 123I-CLINDE SPECT imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, USA (2020)},
	year = {2020},
	author = {Benjamin Auer and Kesava Kalluri and Aly H. Abayazeed and Jan De Beenhouwer and Navid Zeraatkar and Clifford Lindsay and Neil Momsen and R. Garrett Richards and Micaehla May and Matthew A. Kupinski and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {2032,
	title = {Co valence transformation in isopolar LaCoO3/LaTiO3 perovskite heterostructures via interfacial engineering},
	journal = {Phys. Rev. Materials},
	volume = {4},
	year = {2020},
	chapter = {026001},
	doi = {https://doi.org/10.1103/PhysRevMaterials.4.026001},
	author = {G Araizi-Kanoutas and J Geessinck and N Gauquelin and S Smit and X H Verbeeck and S K Mishra and P Bencok and C Schlueter and T-L Lee and D Krishnan and J Fatermans and Jo Verbeeck and G Rijnders and G Koster and M S Golden}
}
@mastersthesis {2341,
	title = { Development of advanced hyperspectral unmixing methods },
	year = {2020},
	type = {PhD thesis},
	author = {Bikram Koirala}
}
@conference {Jillings2020-nb,
	title = {Diffusion MRI reveals macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	year = {2020},
	pages = {4531},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Rumshiskaya, A and Litvinova, L and Nosikova, I and Pechenkova, E and Rukavishnikov, I and Kozlovskaya, I and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, V and Laureys, S and zu Eulenburg, P and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@article {2011,
	title = {The effect of nasal shape on the thermal conditioning of inhaled air: Using clinical tomographic data to build a large-scale statistical shape model},
	journal = {Computers in Biology and Medicine},
	volume = {117},
	year = {2020},
	pages = {1-13},
	doi = {https://doi.org/10.1016/j.compbiomed.2020.103600},
	author = {Keustermans, William and Toon Huysmans and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {2016,
	title = {Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)},
	journal = {Analytica Chimica Acta},
	volume = {1106},
	year = {2020},
	pages = {22-32},
	abstract = {Analytical capabilities of Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS) and Synchrotron Radiation based X-ray Fluorescence (SR nano-XRF) techniques were compared for nanochemical imaging of polymorphonuclear human neutrophils (PMNs). PMNs were high pressure frozen (HPF), cryosubstituted, embedded in Spurr{\textquoteright}s resin and cut in thin sections (500 nm and 2 mm for both techniques resp.) Nano-SIMS enabled nanoscale mapping of isotopes of C, N, O, P and S, while SR based nano-XRF enabled trace level imaging of metals like Ca, Mn, Fe, Ni, Cu and Zn at a resolution of approx. 50 nm.

The obtained elemental distributions were compared with those of whole, cryofrozen PMNs measured at the newly developed ID16A nano-imaging beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Similarities were observed for elements more tightly bound to the cell structure such as phosphorus and sulphur, while differences for mobile ions such as chlorine and potassium were more pronounced. Due to the observed elemental redistribution of mobile ions such as potassium and chlorine, elemental analysis of high pressure frozen (HPF), cryo-substituted and imbedded cells should be interpreted critically. Although decreasing analytical sensitivity occurs due to the presence of ice, analysis of cryofrozen cells - close to their native state - remains the golden standard. In general, we found nanoscale secondary ion mass spectrometry (nano-SIMS) and synchrotron radiation based nanoscopic X-ray fluorescence (SR nano-XRF) to be two supplementary alternatives for nanochemical imaging of single cells at the nanoscale.},
	doi = {10.1016/j.aca.2020.01.054},
	author = {Bjorn De Samber and Riet De Rycke and Michiel De Bruyne and Michiel Kienhuis and Linda Sandblad and Sylvain Bohic and Peter Cloetens and Constantin Urban and Lubos Polerecky and Laszlo Vincze}
}
@inproceedings {2072,
	title = {Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks},
	booktitle = {ICTAI 2020},
	year = {2020},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Johann Kastner and Jan Sijbers}
}
@article {2059,
	title = {How Hyperspectral Image Unmixing and Denoising Can Boost Each Other},
	journal = {Remote Sensing},
	volume = {12},
	year = {2020},
	month = {28 May 2020},
	abstract = {Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets.},
	keywords = {hyperspectral image; unmixing; denoising; linear mixing model; low-rank model; noise reduction; abundance estimation},
	doi = {https://doi.org/10.3390/rs12111728},
	url = {https://www.mdpi.com/2072-4292/12/11/1728$\#$cite},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@article {2083,
	title = {Keel-Edge Height Selection for Improved Multi-Pinhole 123I Brain SPECT Imaging},
	journal = {Journal of Nuclear Medicine},
	volume = {61},
	year = {2020},
	pages = {573},
	abstract = {573Objectives: Given its excellent resolution versus sensitivity trade-off, multi-pinhole SPECT has become a powerful tool for clinical imaging of small human structures such as the brain [1]. Our research team is designing and constructing a next-generation multi-pinhole system, AdaptiSPECT-C, for quantitative brain imaging. In this context, keel-edge pinhole has proven to increase significantly attenuation of gamma rays through the edges of the pinhole aperture compared to the most commonly clinically used knife-edge profile [2-4]. In this work, we investigate the potential improvement in imaging performance of multiple keel-edge pinhole profiles as a function of keel height for AdaptiSPECT-C compared to a knife-edge collimation for 123I-IMP brain perfusion. Methods: The prototype AdaptiSPECT-C system used herein is composed of 23 hexagonal detector modules hemi-spherically arranged along 3 rings. For modeling in GATE simulation (GS) [5], each of these modules is composed of 1.5 mm radius pinhole and a 1 cm thick NaI(Tl) crystal with a 5 cm thick back-scattering compartment, which was considered to simulate 123I down-scatter interactions. Multiple keel-edge heights, corresponding to 0.0 (knife edge), 0.375, 0.75, 1.0, 1.125, 1.5, 1.875, and 2.25 mm were studied. We evaluated the volumetric sensitivity and relative amount of collimator penetration for a 15\% energy window centered at 159 keV in simulated projections of a 21 cm diameter spherical source (e.g. corresponding to the system{\textquoteright}s volume of interest) centered at the focal point of the pinholes. For reconstruction, an approach developed in our group was employed for modeling using GS the system response and especially collimator penetration into the system matrix [6,7] for the knife and the keel-edge designs. An XCAT [8] brain phantom with source distribution for the perfusion imaging agent 123I-IMP was simulated using the pinhole designs. Projection were acquired considering two scenarios: noise free (S1), and equal imaging time comparison for a realistic clinical scan time (e.g. 30 min [9,10]) (S2). Reconstructions were performed with a customized 3D-MLEM software into images of 1203 voxels of (2 mm)3. The reconstructed images were then compared to the ground truth image in terms of the normalized root mean squared error (NRMSE) and activity recovery (\%AR) for selected three-dimensional brain regions. Results: A keel-edge height of 0.375-0.75 mm represents the best choice leading to a significant reduction of the amount of penetration (up to  50\%) at the expense of sensitivity (-20\%) compared to a knife-edge profile. Visually, for all scenarios, the use of such a keel-edge profile leads to better separation of the brain structures, especially the caudate and the putamen. When sensitivity is not taken into account (e.g. noise free scenario), increasing the keel height improves NRMSE results. For an equal imaging time comparison, lowest NRMSE values are achieved for a 0.375-0.75 mm keel-height. A further keel-height increase degrades the NRMSE results due to significant loss of counts compared to knife-edge design. A 0.75 mm keel height leads on average to the best \%ARs (e.g. closest value to 100\%), especially for the striatum and putamen. For cortex and cerebellum regions, \%ARs are comparable with those obtained for a knife-edge design. Conclusion: In this work, we demonstrated that the use of a  0.75 mm height keel-edge profile for AdaptiSPECT-C incorporating 1.5 mm radius pinholes leads to superior imaging performance compared to knife-edge collimation for clinical 123I brain perfusion imaging. A range of aperture radii from 0.5 to 3.5 mm for each design have been investigated and will be shown at the time of the conference. We are currently working on performing a numerical-observer task-performance study of defect-detection in perfusion. Research Support: National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Grant No R01 EB022521. Volumetric Sensitivities, Amount of Penetration, and Lowest NRMSE for the designs investigated},
	url = {http://jnm.snmjournals.org/content/61/supplement_1/573.abstract},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Neil Momsen and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {2048,
	title = {A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {58},
	year = {2020},
	month = {04/2020},
	pages = {7393-7405},
	abstract = {Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400-2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set.},
	doi = {10.1109/TGRS.2020.2982263},
	url = {https://www.researchgate.net/publication/340391024_A_Machine_Learning_Framework_for_Estimating_Leaf_Biochemical_Parameters_From_Its_Spectral_Reflectance_and_Transmission_Measurements},
	author = {Bikram Koirala and Zohreh Zahiri and Paul Scheunders}
}
@article {2074,
	title = {Macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	journal = {Science Advances},
	volume = {6},
	year = {2020},
	month = {Apr-09-2020},
	pages = {eaaz9488},
	abstract = {Long-duration spaceflight causes widespread physiological changes, although its effect on brain structure remains poorly understood. In this work, we acquired diffusion magnetic resonance imaging to investigate alterations of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compositions in each voxel, before, shortly after, and 7 months after long-duration spaceflight. We found increased WM in the cerebellum after spaceflight, providing the first clear evidence of sensorimotor neuroplasticity. At the region of interest level, this increase persisted 7 months after return to Earth. We also observe a widespread redistribution of CSF, with concomitant changes in the voxel fractions of adjacent GM. We show that these GM changes are the result of morphological changes rather than net tissue loss, which remained unclear from previous studies. Our study provides evidence of spaceflight-induced neuroplasticity to adapt motor strategies in space and evidence of fluid shift{\textendash}induced mechanical changes in the brain.},
	doi = {10.1126/sciadv.aaz9488},
	url = {https://advances.sciencemag.org/content/6/36/eaaz9488},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Kozlovskaya, Inessa B. and Manko, Olga and Danilichev, Sergey and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, Victor and Laureys, Steven and zu Eulenburg, Peter and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@conference {2019,
	title = {Optimal design of a T1 super-resolution reconstruction experiment: a simulation study},
	year = {2020},
	author = {Michele Nicastro and Quinten Beirinckx and Piet Bladt and Ben Jeurissen and Stefan Klein and Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker}
}
@article {2017,
	title = {PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping},
	journal = {PlosOne},
	volume = {15},
	year = {2020},
	pages = {e0229685},
	doi = {https://doi.org/10.1371/journal.pone.0229685},
	author = {Brian G Booth and Eva Hoefnagels and Toon Huysmans and Jan Sijbers and No{\"e}l L.W. Keijsers}
}
@article {2082,
	title = {Reconstruction using Depth of Interaction Information of Curved and Flat Detector Designs for Quantitative Multi-Pinhole Brain SPECT},
	journal = {Journal of Nuclear Medicine},
	volume = {61},
	year = {2020},
	pages = {103},
	abstract = {103Objectives: Brain SPECT has many clinical applications, especially for cerebral blood flow and dopamine transporter imaging [1,2]. In this context, a dedicated brain imaging, multi-pinhole system, AdaptiSPECT-C, is being developed by our group. Recent studies in cardiac and small animal imaging have demonstrated that the use of curved detector could improve image quality, by reducing parallax errors due to the depth of interaction (DOI) effect [3,4]. In this simulation study, we proposed to investigate using reconstruction with DOI modeling the potential advantage in imaging performance of curved over flat detectors for 123I-IMP perfusion imaging using the AdaptiSPECT-C system. Methods: The AdaptiSPECT-C design used in this work consists of 26 detector modules, 158 by 158 mm2 in size, arranged around the patient{\textquoteright}s head in three rings. The simulated detector modules were composed of a 8 mm thick NaI(Tl) crystal coupled to a 5 cm thick back-scattering compartment, representing components behind the crystal, to model 123I down-scatter interactions. Each detector module is associated with a 1.36 mm radius direct knife-edge pinhole aperture collimator. Two different system designs were considered, one based on curved detectors, and the other on flat detectors. The curved detectors were designed so that the radius of curvature corresponds to the detector to system center distance (e.g. 30.5 cm). This distance was the same for the flat detectors. GATE simulation [5] was employed to compute the system matrix [6,7] for both detector designs by forming the system response for the activity within each three-dimensional image-voxel of a 24 cm diameter sphere, thus including DOI variations and corrections in reconstruction [6,7]. An XCAT brain phantom [8] emulating 123I-IMP perfusion source distribution was simulated using the two designs. Data were acquired following three scenarios: noise free case (S1), equal number of counts comparison (S2) (e.g. 5.5M detected counts [9]), and equal imaging time comparison for the typical scan time (e.g. 30 min [10]) (S3). For S3, the total number of counts for the curved and flat detector designs, were respectively 9.24M and 9.17M. Projections were reconstructed with 3D-MLEM into images of 1203 voxels of (2 mm)3 and reconstruction compared to the ground truth image. The normalized root mean squared error (NRMSE) as well as percentage of activity recovery (\%AR) for several brain regions were used to evaluate the image quality. Results: Only a small gain in volumetric sensitivity ( 0.8\%) was obtained with the curved detector design. Qualitatively, the reconstructions for the curved and flat detector designs appear similar for all the 3 noise scenarios. Differences are mostly for the peripheral regions of the head where the differences in the obliquity of the gamma-rays passing through the apertures would be the greatest. Due to lower activity, those regions are also more impacted by noise. Quantitatively, a slight NRMSE improvement using curved detectors was seen. The curved detector design leads on average to the best ARs, especially for the striatum and putamen. Regions at the edges of the brain (e.g. cortex and cerebellum), more impacted by DOI effect, are similarly recovered by the two designs. Conclusion: We demonstrated that using curved instead of flat detector for AdaptiSPECT-C with solely centered pinholes leads to small improvement in sensitivity and image quality based on visual inspection, NRMSE, and activity recovery analysis. Flat detector associated with a sophisticated DOI correction was found to lead to similar results than those obtained with the curved detector. Further investigation will be performed using additional pinholes irradiating the 4 quadrants of the detectors which will increase the obliquity of the rays striking the detectors and may thus result in larger difference. Research Support: Grant No R01 EB022521 (NIBIB).},
	url = {http://jnm.snmjournals.org/content/61/supplement_1/103.abstract},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Kimberly Doty and Navid Zeraatkar and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@conference {1924,
	title = {Advancing Analysis Techniques for Plantar Pressure Videos via Open-Access Contributions},
	year = {2019},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Toon Huysmans and Jan Sijbers}
}
@article {1964,
	title = {Alterations of Functional Brain Connectivity After Long-Duration Spaceflight as Revealed by fMRI.},
	journal = {Front Physiol},
	volume = {10},
	year = {2019},
	pages = {1-23},
	abstract = {<p>The present study reports alterations of task-based functional brain connectivity in a group of 11 cosmonauts after a long-duration spaceflight, compared to a healthy control group not involved in the space program. To elicit the postural and locomotor sensorimotor mechanisms that are usually most significantly impaired when space travelers return to Earth, a plantar stimulation paradigm was used in a block design fMRI study. The motor control system activated by the plantar stimulation involved the pre-central and post-central gyri, SMA, SII/operculum, and, to a lesser degree, the insular cortex and cerebellum. While no post-flight alterations were observed in terms of activation, the network-based statistics approach revealed task-specific functional connectivity modifications within a broader set of regions involving the activation sites along with other parts of the sensorimotor neural network and the visual, proprioceptive, and vestibular systems. The most notable findings included a post-flight increase in the stimulation-specific connectivity of the right posterior supramarginal gyrus with the rest of the brain; a strengthening of connections between the left and right insulae; decreased connectivity of the vestibular nuclei, right inferior parietal cortex (BA40) and cerebellum with areas associated with motor, visual, vestibular, and proprioception functions; and decreased coupling of the cerebellum with the visual cortex and the right inferior parietal cortex. The severity of space motion sickness symptoms was found to correlate with a post- to pre-flight difference in connectivity between the right supramarginal gyrus and the left anterior insula. Due to the complex nature and rapid dynamics of adaptation to gravity alterations, the post-flight findings might be attributed to both the long-term microgravity exposure and to the readaptation to Earth{\textquoteright}s gravity that took place between the landing and post-flight MRI session. Nevertheless, the results have implications for the multisensory reweighting and gravitational motor system theories, generating hypotheses to be tested in future research.</p>},
	issn = {1664-042X},
	doi = {10.3389/fphys.2019.00761},
	author = {Pechenkova, Ekaterina and Nosikova, Inna and Rumshiskaya, Alena and Litvinova, Liudmila and Rukavishnikov, Ilya and Mershina, Elena and Valentin Sinitsyn and Angelique Van Ombergen and Ben Jeurissen and Steven Jillings and Laureys, Steven and Jan Sijbers and Grishin, Alexey and Chernikova, Ludmila and Naumov, Ivan and Kornilova, Ludmila and Floris L Wuyts and Tomilovskaya, Elena and Kozlovskaya, Inessa}
}
@article {1927,
	title = {An assessment of the information lost when applying data reduction techniques to dynamic plantar pressure measurements},
	journal = {Journal of Biomechanics },
	volume = {87},
	year = {2019},
	pages = {161-166},
	doi = {https://doi.org/10.1016/j.jbiomech.2019.02.008},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@article {1941,
	title = {Brain ventricular volume changes induced by long-duration spaceflight},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	pages = {10531-10536},
	abstract = {Long-duration spaceflight induces detrimental changes in human physiology due to microgravity. One example is a cephalic fluid shift. Here, we prospectively investigated the quantitative changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest, observer-independent analysis on structural brain MRI scans. MRI scans were collected before the mission, shortly after and 7 mo after return to Earth. We found a significant increase in lateral and third ventricles at postflight and a trend to normalization at follow-up, but still significantly increased ventricular volumes. The observed spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause.Long-duration spaceflight induces detrimental changes in human physiology. Its residual effects and mechanisms remain unclear. We prospectively investigated the changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest analysis on structural brain scans. Cosmonaut MRI data were investigated preflight (n = 11), postflight (n = 11), and at long-term follow-up 7 mo after landing (n = 7). Post hoc analyses revealed a significant difference between preflight and postflight values for all supratentorial ventricular structures, i.e., lateral ventricle (mean \% change {\textpm} SE = 13.3 {\textpm} 1.9), third ventricle (mean \% change {\textpm} SE = 10.4 {\textpm} 1.1), and the total ventricular volume (mean \% change {\textpm} SE = 11.6 {\textpm} 1.5) (all P \&lt; 0.0001), with higher volumes at postflight. At follow-up, these structures did not quite reach baseline levels, with still residual increases in volume for the lateral ventricle (mean \% change {\textpm} SE = 7.7 {\textpm} 1.6; P = 0.0009), the third ventricle (mean \% change {\textpm} SE = 4.7 {\textpm} 1.3; P = 0.0063), and the total ventricular volume (mean \% change {\textpm} SE = 6.4 {\textpm} 1.3; P = 0.0008). This spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause. Our results warrant more detailed and longer longitudinal follow-up. The clinical impact of our findings on the long-term cosmonauts{\textquoteright} health and their relation to ocular changes reported in space travelers requires further prospective studies.},
	issn = {0027-8424},
	doi = {10.1073/pnas.1820354116},
	url = {https://www.pnas.org/content/early/2019/05/01/1820354116},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Alena Rumshiskaya and Liudmila Litvinova and Nosikova, Inna and Ekaterina V. Pechenkova and Ilya Rukavishnikov and Manko, Olga and Danylichev, Sergey and R{\"u}hl, R. Maxine and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@conference {1984,
	title = {A deep learning approach to T1 mapping in quantitative MRI},
	volume = {32 (Suppl. 1)},
	number = {S09.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {Quantitative MRI aims to measure biophysical tissue parameters through the analysis of the MR signal. Conventional parameter estimation methods, which often rely on a voxel-wise mapping, ignores spatial redundancies. In this work, a deep learning method for T1 mapping is proposed to overcome this limitation.},
	doi = {10.1007/s10334-019-00754-2},
	author = {Ribeiro Sabidussi, Emanoel and Michele Nicastro and Shabab Bazrafkan and Quinten Beirinckx and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker and Stefan Klein and Dirk H J Poot}
}
@inproceedings {1850,
	title = {Determining Comfortable Pressure Ranges for Wearable EEG Headsets},
	booktitle = {Advances in Human Factors in Wearable Technologies and Game Design},
	year = {2019},
	pages = {11{\textendash}19},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Measuring and interpretation of brain wave signals through electroencephalography (EEG) is an emerging technology. The technique is traditionally applied in a clinical setting with EEG caps and conductive gels to ensure proper contact through a subject{\textquoteright}s hair, and anticipate inter-subject anthropometric variations. Development of dry electrodes offers the potential to develop wearable EEG headsets. Such devices could induce medical and commercial applications. In this paper, we evaluate a prototype EEG headset that actively places electrodes at standardized positions on the subject{\textquoteright}s head, where each electrode is applied with equal pressure. The system is designed for use with dry electrodes. Our research delivers a better understanding on the link between general level of comfort and possible useful clear data signals, that can be used in brain computer interfaces (BCI). The present study is confined to the impact of adjustable electrodes pressure on level of user comfort only. Levels of discomfort are assessed in twelve participants, wearing an EEG headset with controllable electrode pressure exerted at 14 locations. Of-the-shelf dry electrodes are used. In a first session, evenly distributed pressure is increased and afterwards decreased in fixed time intervals, going from 10~kPa to 30~kPa and vice versa with steps of 2~kPa. In a second session, a subject specific acceptable pressure level is retrieved from the data of the first session and constantly applied for 30~min. During this intervention, level of discomfort is assessed in a VAS-scale. Additional observation and surveys yields insights on user experience in wearing a pressure exerting EEG headset.},
	isbn = {978-3-319-94619-1},
	author = {Stijn Verwulgen and Daniel Lacko and Justine, Hoppenbrouwers and Kustermans, Siemon and Moons, Stine and Thys, Falk and Zelck, Sander and Vaes, Kristof and Toon Huysmans and Jochen Vleugels and Steven Truijen},
	editor = {Ahram, Tareq Z.}
}
@inproceedings {1986,
	title = {Fractional abundance estimation of mixed and compound materials by hyperspectral imaging.},
	booktitle = {10th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Sept 2019},
	year = {2019},
	pages = {pp. 1-5},
	address = {Amsterdam, Netherlands},
	abstract = {The mechanical and chemical properties of a compound material are determined by the fractional abundances of its components. In this work, we present a spectral unmixing technique to estimate the fractional abundances of the components of mixed and compound materials from hyperspectral images. The estimation of fractional abundances in mixed materials faces the main challenge of intimate mixing. In compound materials, the mixing with water causes changes in chemical properties resulting in spectral variability and non-linearity. To address these challenges, a supervised method is proposed that learns a mapping from the hyperspectral data to spectra that follow the linear mixing model. Then, a linear unmixing technique is applied on the mapped spectra to estimate the fractional abundances. To demonstrate the potential of the proposed method, experiments are conducted on hyperspectral images from mixtures of red and yellow clay powders and hardened mortar samples with varying water to cement ratios.},
	doi = {doi: 10.1109/WHISPERS.2019.8920893},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=8920893\&isnumber=8920817},
	author = {Bikram Koirala and Zohreh Zahiri and Mahdi Khodadadzadeh and Paul Scheunders}
}
@article {1991,
	title = {Importance of pressure plasticity during compression of probiotic tablet formulations},
	journal = {European Journal of Pharmaceutics and Biopharmaceutics},
	volume = {145},
	year = {2019},
	pages = {7-11},
	doi = {https://doi.org/10.1016/j.ejpb.2019.10.001},
	author = {E Byl and Piet Bladt and S Lebeer and Filip Kiekens}
}
@inproceedings {1918,
	title = {An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	author = {Johannes Weissenb{\"o}ck and Bernhard Fr{\"o}hler and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Santhosh Ayalur Karunakaran and Helmuth Hoeller and Johann Kastner and Christoph Heinzl}
}
@inproceedings {1989,
	title = {Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging},
	booktitle = {15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	volume = {11072},
	year = {2019},
	pages = {187 {\textendash} 190},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	keywords = {image reconstruction, Monte-Carlo simulation, SPECT I-123 brain imaging, System response modeling, Variance reduction technique (forced detection)},
	doi = {10.1117/12.2534881},
	url = {https://doi.org/10.1117/12.2534881},
	author = {Benjamin Auer and Navid Zeraatkar and Jan De Beenhouwer and Kesava Kalluri and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@conference {1990,
	title = {Investigation of keel versus knife edge pinhole profiles for a next-generation SPECT system dedicated to clinical brain imaging},
	year = {2019},
	month = {2019},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Arda K{\"o}nik and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {1914,
	title = {Matlab{\textregistered} toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling},
	journal = {Computers in Biology and Medicine},
	volume = {105},
	year = {2019},
	pages = {27-38},
	doi = {10.1016/j.compbiomed.2018.12.008},
	author = {Keustermans, William and Toon Huysmans and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {1916,
	title = {Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content},
	journal = {Journal of Magnetic Resonance},
	volume = {300},
	year = {2019},
	pages = {18-27},
	doi = {10.1016/j.jmr.2018.12.019},
	author = {Maarten Naeyaert and Dimitri Roose and Zhenhua Mai and Aneta J Keliris and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@inproceedings {1988,
	title = {Preliminary investigation of attenuation and scatter correction strategies for a next-generation SPECT system dedicated to quantitative clinical brain imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2019},
	month = {11/2019},
	address = {Manchester, UK},
	author = {Benjamin Auer and Jan De Beenhouwer and Navid Zeraatkar and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {2085,
	title = {Primary, scatter, and penetration characterizations of parallel-hole and pinhole collimators for I-123 SPECT},
	journal = {Physics in Medicine \& Biology},
	volume = {64},
	year = {2019},
	pages = {245001},
	abstract = {Multi-pinhole (MPH) collimators are known to provide better trade-off between sensitivity and resolution for preclinical, as well as for smaller regions in clinical SPECT imaging compared to conventional collimators. In addition to this geometric advantage, MPH plates typically offer better stopping power for penetration than the conventional collimators, which is especially relevant for I-123 imaging. The I-123 emits a series of high-energy (>300 keV,  2.5\% abundance) gamma photons in addition to the primary emission (159 keV, 83\% abundance). Despite their low abundance, high-energy photons penetrate through a low-energy parallel-hole (LEHR) collimator much more readily than the 159 keV photons, resulting in large downscatter in the photopeak window. In this work, we investigate the primary, scatter, and penetration characteristics of a single pinhole collimator that is commonly used for I-123 thyroid imaging and our two MPH collimators designed for I-123 DaTscan imaging for Parkinson{\textquoteright}s Disease, in comparison to three different parallel-hole collimators through a series of experiments and Monte Carlo simulations. The simulations of a point source and a digital human phantom with DaTscan activity distribution showed that our MPH collimators provide superior count performance in terms of high primary counts, low penetration, and low scatter counts compared to the parallel-hole and single pinhole collimators. For example, total scatter, multiple scatter, and collimator penetration events for the LEHR were 2.5, 7.6 and 14 times more than that of MPH within the 15\% photopeak window. The total scatter fraction for LEHR was 56\% where the largest contribution came from the high-energy scatter from the back compartments (31\%). For the same energy window, the total scatter for MPH was 21\% with only 1\% scatter from the back compartments. We therefore anticipate that using MPH collimators, higher quality reconstructions can be obtained in a substantially shorter acquisition time for I-123 DaTscan and thyroid imaging.},
	doi = {10.1088/1361-6560/ab58fe},
	url = {https://doi.org/10.1088\%2F1361-6560\%2Fab58fe},
	author = {Arda K{\"o}nik and Benjamin Auer and Jan De Beenhouwer and Kesava Kalluri and Navid Zeraatkar and Lars R. Furenlid and Michael A. King}
}
@inproceedings {1985,
	title = {A semi-supervised method for Nonlinear Hyperspectral Unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 2019},
	year = {2019},
	pages = {pp. 361-364},
	abstract = {As the interaction of light with the Earth surface is very complex, spectral reflectances are composed of nonlinear mixtures of the observed materials. Nonlinear mixing models have the disadvantage that not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. Moreover, most models lack a proper interpretation of the estimated parameters in terms of fractional abundances.  
In this paper, we present a semi-supervised nonlinear unmixing technique that overcomes these problems. In a first step, we apply a kernelized simplex volume maximization to select an overcomplete set of endmembers that precisely describe the hyperspectral data manifold. 
In a second step, this set is used as ground truth data in a supervised learning approach to generate fractional abundance maps from the entire dataset. For this, three methods are presented, based on kernelized sparse unmixing, feedforward neural networks, and gaussian processes.
The proposed method is validated on simulated data, a dataset obtained by ray tracing, and a real hyperspectral image.},
	doi = {doi: 10.1109/IGARSS.2019.8898846},
	author = {Bikram Koirala and Paul Scheunders}
}
@inproceedings {1884,
	title = {Simulated grating-based x-ray phase contrast images of CFRP-like objects},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	pages = {1-8},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_45.pdf},
	author = {Jonathan Sanctorum and Jan De Beenhouwer and Johannes Weissenb{\"o}ck and Christoph Heinzl and Johann Kastner and Jan Sijbers}
}
@article {1987,
	title = {A supervised method for nonlinear hyperspectral unmixing},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	month = {10/2019},
	abstract = {Due to the complex interaction of light with the Earth surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occuring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning the nonlinear mapping, based on gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset show that this novel methodology is very promising.},
	doi = {https://doi.org/10.3390/rs11202458},
	url = {https://www.mdpi.com/2072-4292/11/20/2458},
	author = {Bikram Koirala and Mahdi Khodadadzadeh and Cecilia Contreras and Zohreh Zahiri and Richard Gloaguen and Paul Scheunders}
}
@inproceedings {1917,
	title = {Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_52.pdf},
	author = {Bernhard Fr{\"o}hler and Lucas da Cunha Melo and Johannes Weissenb{\"o}ck and Johann Kastner and Torsten M{\"o}ller and Hans-Christian Hege and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Christoph Heinzl}
}
@article {1923,
	title = {A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science},
	journal = {Computer Graphics Forum},
	volume = {38},
	year = {2019},
	pages = {273-283},
	doi = {10.1111/cgf.13688},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Johannes Weissenb{\"o}ck and Jan De Beenhouwer and Jan Sijbers and Hans-Christian Hege and Johann Kastner and Christoph Heinzl}
}
@article {1908,
	title = {Brain Tissue{\textendash}Volume Changes in Cosmonauts},
	journal = {New England Journal of Medicine},
	volume = {379},
	year = {2018},
	pages = {1678 - 1680},
	issn = {0028-4793},
	doi = {10.1056/NEJMc1809011},
	url = {http://www.nejm.org/doi/10.1056/NEJMc1809011},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and R{\"u}hl, Maxine and Alena Rumshiskaya and Nosikova, Inna and Liudmila Litvinova and Annen, Jitka and Ekaterina V. Pechenkova and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@inproceedings {1831,
	title = {Comparative Visualization of Orientation Tensors in Fiber-Reinforced Polymers},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria },
	year = {2018},
	author = {Johannes Weissenb{\"o}ck and M Arikan and D Salaberger and Johann Kastner and Jan De Beenhouwer and Jan Sijbers and S Rauchenzauner and T Raab-Wernig and E Gr{\"o}ller and Christoph Heinzl}
}
@inproceedings {1895,
	title = {Fusion of hyperspectral and Lidar images using non-subsampled shearlet transform},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {M. Reza Soleimanzadeh and Azam Karami and Paul Scheunders}
}
@article {1912,
	title = {High quality statistical shape modelling of the human nasal cavity and applications},
	journal = {Royal Society Open Science},
	volume = {5},
	year = {2018},
	doi = {https://doi.org/10.1098/rsos.181558},
	author = {Keustermans, William and Toon Huysmans and Femke Danckaers and Andrzej Zarowski and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@inproceedings {1896,
	title = {Hyperspectral and multispectral image fusion based on spectral matching in the shearlet domain},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {H. Rezaei and Azam Karami and Paul Scheunders}
}
@conference {1860,
	title = {Investigating the relationship between shape and flow in the human nose using a statistical shape model},
	year = {2018},
	url = {http://www.eccm-ecfd2018.org/admin/files/fileabstract/a781.pdf},
	author = {Keustermans, William and Toon Huysmans and Femke Danckaers and Andrzej Zarowski and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@inproceedings {1875,
	title = {A NEURAL NETWORK METHOD FOR NONLINEAR HYPERSPECTRAL UNMIXING},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	pages = {pp. 4233-4236},
	address = {Valencia, Spain},
	abstract = {Because of the complex interaction of light with the Earth surface, a hyperspectral pixel can be composed of a highly nonlinear mixture of the reflectances of the materials on the ground. When nonlinear mixing models are applied, the estimated model parameters are usually hard to interpret and to link to the actual fractional abundances. Moreover, not all spectral reflectances in a real scene follow the same particular mixing model. In this paper, we present a supervised learning method for nonlinear spectral unmixing. In this method, a neural network is applied to learn mappings of the true spectral reflectances to the reflectances that would be obtained if the mixture was linear. A simple linear unmixing then reveals the actual abundance fractions. This technique is model-independent and allows for an easy interpretation of the obtained abundance fractions. We validate this method on several artificial datasets, a data set obtained by ray tracing, and a real dataset.},
	doi = {doi: 10.1109/IGARSS.2018.8518995},
	url = {https://ieeexplore.ieee.org/document/8518995},
	author = {Bikram Koirala and Rob Heylen and Paul Scheunders}
}
@inproceedings {1880,
	title = {Preliminary evaluation of surface mesh modeling of system geometry, anatomy phantom, and source activity for GATE simulations},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	month = {11/2018},
	address = {Sydney, Australia},
	author = {Benjamin Auer and Arda K{\"o}nik and Timothy J. Fromme and Kesava Kalluri and Jan De Beenhouwer and George I. Zubal and Lars R. Furenlid and Michael A. King}
}
@inproceedings {1879,
	title = {Preliminary investigation of design parameters of an innovative multi- pinhole system dedicated to brain SPECT imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	month = {11/2018},
	address = {Sydney, Australia},
	author = {Benjamin Auer and Jan De Beenhouwer and Timothy J. Fromme and Kesava Kalluri and Justin C. Goding and George I. Zubal and Lars R. Furenlid and Michael A. King}
}
@article {1877,
	title = {Simulations of a Multipinhole SPECT Collimator for Clinical Dopamine Transporter (DAT) Imaging},
	journal = {IEEE Transactions on Radiation and Plasma Medical Sciences},
	volume = {2},
	year = {2018},
	month = {Sept},
	pages = {444-451},
	keywords = {Analytical models, Apertures, Brain, Collimators, Logic gates, multipinhole (MPH), Sensitivity, simulation, Single photon emission computed tomography, SPECT},
	issn = {2469-7311},
	doi = {10.1109/TRPMS.2018.2831208},
	author = {Arda K{\"o}nik and Jan De Beenhouwer and J. M. Mukherjee and Kesava Kalluri and S Banerjee and N Zeraatkar and Timothy J. Fromme and Michael A. King}
}
@article {1837,
	title = {STAPP: SpatioTemporal Analysis of Plantar Pressure Measurements using Statistical Parametric Mapping},
	journal = {Gait and Posture},
	volume = {3},
	year = {2018},
	pages = {268-275},
	abstract = {Background: Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures.

Research Question: We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions.

Methods: To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds.

Results: As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases have not previously been observed using existing plantar pressure analysis techniques.

Significance: We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP.},
	doi = {10.1016/j.gaitpost.2018.04.029},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@article {1865,
	title = {A three-dimensional digital neurological atlas of the mustached bat (Pteronotus parnellii)},
	journal = {NeuroImage},
	volume = {183},
	year = {2018},
	pages = {300-313},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2018.08.013},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811918307110https://api.elsevier.com/content/article/PII:S1053811918307110?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1053811918307110?httpAccept=text/plain},
	author = {Washington, Stuart D. and Hamaide, Julie and Ben Jeurissen and Gwendolyn Van Steenkiste and Toon Huysmans and Jan Sijbers and Deleye, Steven and Kanwal, Jagmeet S. and De Groof, Geert and Liang, Sayuan and Johan Van Audekerke and Wenstrup, Jeffrey J. and Annemie Van Der Linden and Radtke-Schuller, Susanne and Marleen Verhoye}
}
@article {1915,
	title = {The arcuate fasciculus network and verbal deficits in psychosis},
	journal = {Translational Neuroscience},
	volume = {8},
	year = {2017},
	month = {Feb-11-2017},
	pages = {117-126},
	doi = {10.1515/tnsci-2017-0018},
	author = {Kenney, Joanne P.M. and McPhilemy, Genevieve and Scanlon, Cathy and Najt, Pablo and McInerney, Shane and Arndt, Sophia and Scherz, Elisabeth and Byrne, Fintan and Alexander Leemans and Ben Jeurissen and Hallahan, Brian and McDonald, Colm and Cannon, Dara M.}
}
@inproceedings {1771,
	title = {A Comparison Between Physical and Virtual Experiments of Convective Heat Transfer Between Head and Bicycle Helmet},
	booktitle = {8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	author = {Shriram Mukunthan and Kalev Kuklane and Toon Huysmans and Guido De Bruyne}
}
@article {1756,
	title = {The effect of spaceflight and microgravity on the human brain},
	journal = {Journal of Neurology},
	volume = {246},
	year = {2017},
	pages = {18-22},
	doi = {10.1007/s00415-017-8427-x},
	author = {Angelique Van Ombergen and Athena Demertzi and Elena Tomilovskaya and Ben Jeurissen and Jan Sijbers and Inessa B. Kozlovskaya and Paul M Parizel and Paul H Van de Heyning and Stefan Sunaert and Steven S L Laureys and Floris L Wuyts}
}
@inproceedings {1753,
	title = {Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	url = {http://www.ndt.net/events/iCT2017/app/content/Extended_Abstract/63_Janssens_Rev4.pdf},
	author = {Eline Janssens and Sascha Senck and Christoph Heinzl and Johann Kastner and Jan De Beenhouwer and Jan Sijbers}
}
@article {1802,
	title = {Habitat mapping and quality assessment of NATURA 2000 Heatland using airborne imaging spectroscopy},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {266},
	author = {Birgen Haest and Jeroen Vanden Borre and Toon Spanhove and Guy Thoonen and Stephanie Delalieux and Kooistra, L. and C A M{\"u}cher and D Paelinckx and Paul Scheunders and Pieter Kempeneers}
}
@article {1718,
	title = {High angular resolution diffusion-weighted imaging in mild traumatic brain injury},
	journal = {NeuroImage: Clinical},
	volume = {13},
	year = {2017},
	month = {11/2016},
	pages = {174 - 180},
	abstract = {We sought to investigate white matter abnormalities in mild traumatic brain injury (mTBI) using diffusion-weighted magnetic resonance imaging (DW-MRI). We applied a global approach based on tract-based spatial statistics skeleton as well as constrained spherical deconvolution tractography. DW-MRI was performed on 102 patients with mTBI within two months post-injury and 30 control subjects. A robust global approach considering only the voxels with a single-fiber configuration was used in addition to global analysis of the tract skeleton and probabilistic whole-brain tractography. In addition, we assessed whether the microstructural parameters correlated with age, time from injury, patient{\textquoteright}s outcome and white matter \{MRI\} hyperintensities. We found that whole-brain global approach restricted to single-fiber voxels showed significantly decreased fractional anisotropy (FA) (p = 0.002) and increased radial diffusivity (p = 0.011) in patients with mTBI compared with controls. The results restricted to single-fiber voxels were more significant and reproducible than those with the complete tract skeleton or the whole-brain tractography. \{FA\} correlated with patient outcomes, white matter hyperintensities and age. No correlation was observed between \{FA\} and time of scan post-injury. In conclusion, the global approach could be a promising imaging biomarker to detect white matter abnormalities following traumatic brain injury.},
	keywords = {Diffusion-weighted magnetic resonance imaging, Global approach, Magnetic Resonance Imaging, probabilistic tractography, Traumatic brain injury},
	issn = {2213-1582},
	doi = {http://dx.doi.org/10.1016/j.nicl.2016.11.016},
	url = {http://www.sciencedirect.com/science/article/pii/S2213158216302248},
	author = {Mohammadian, Mehrbod and Timo Roine and Jussi Hirvonen and Timo Kurki and Henna Ala-Sepp{\"a}l{\"a} and Janek Frantz{\'e}n and Ari Katila and Anna Kyll{\"o}nen and Henna-Riikka Maanp{\"a}{\"a} and Jussi Posti and Riikka Takala and Jussi Tallus and Olli Tenovuo}
}
@article {1775,
	title = {A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps},
	journal = {Signal Image and Video Processing},
	volume = {11},
	year = {2017},
	pages = { 913-920},
	author = {P V Sudeep and Palanisamy, P and Chandrasekharan Kesavadas and Jan Sijbers and Arnold Jan den Dekker and Jeny Rajan}
}
@article {1804,
	title = {Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {541},
	author = {E.K. Ghasrodashti and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1644,
	title = {3D morphometric analysis of the human incudomallear complex using clinical cone-beam CT},
	journal = {Hearing research},
	volume = {340},
	year = {2016},
	pages = {79-88},
	abstract = {Human middle ears show large morphological variations. This could affect our perception of hearing and explain large variation in experimentally obtained transfer functions. Most morphological studies focus on capturing variation by using landmarks on cadaveric temporal bones. We present statistical shape analysis based on clinical cone beam CT (CBCT) scans of 100 patients. This allowed us to include surface information on the incudomallear (IM) complex (joint, ligaments and tendon not included) of 123 healthy ears with a scanning resolution of 150~μm and without a priori assumptions. Statistical shape modeling yields an average geometry for the IM complex and the variations present in the population with a high precision. Mean values, variation and correlations among anatomical features (length of manubrium, combined length of malleus head and neck, lengths of incus long and short process, enclosing angles, ossicular lever ratio, incudomallear angle, and principal moments of inertia) are reported and compared to results from the literature. Most variation is found in overall size and the angle between incus and malleus. The compact representation provided by statistical shape modeling is demonstrated and its benefits for surface modeling are discussed.},
	issn = {1878-5891},
	doi = {10.1016/j.heares.2016.01.014},
	author = {Joris Soons and Femke Danckaers and Keustermans, William and Toon Huysmans and Jan Sijbers and Casselman, Jan W and Joris J. J. Dirckx}
}
@article {1686,
	title = {Advanced techniques for computational and information sciences},
	journal = {Mathematical Problems in Engineering},
	volume = {2016},
	year = {2016},
	edition = {Article ID 5623913},
	author = {W. Guo and C.-C. Hung and Paul Scheunders and B.-C. Kuo and Y. Lan and Z. Ma}
}
@article {1683,
	title = {Chronic exposure to haloperidol and olanzapine leads to common and divergent shape changes in the rat hippocampus in the absence of grey-matter volume loss},
	journal = {Psychological Medicine},
	volume = {46},
	year = {2016},
	pages = {3081-3093},
	doi = {10.1017/S0033291716001768},
	author = {William R Crum and Femke Danckaers and Toon Huysmans and Marie-Caroline Cotel and Sridhar Natesan and Michel M. Modo and Jan Sijbers and Steven C.R. Williams and Shitij Kapur and Anthony C. Vernon}
}
@inproceedings {1700,
	title = {Compression Of Hyperspectral Images Using Block Coordinate Descent Search And Compressed Sensing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = {Los Angeles, USA},
	author = {Shirin Hassanzadeh and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1584,
	title = {Cortical reorganization in an astronaut{\textquoteright}s brain after long-duration spaceflight},
	journal = {Brain Structure and Function},
	volume = {221},
	year = {2016},
	pages = {2873{\textendash}2876},
	doi = {10.1007/s00429-015-1054-3},
	author = {Athena Demertzi and Angelique Van Ombergen and Elena Tomilovskaya and Ben Jeurissen and Ekaterina V. Pechenkova and Carol Di Perri and Liudmila Litvinova and Enrico Amico and Alena Rumshiskaya and Ilya Rukavishnikov and Jan Sijbers and Valentin Sinitsyn and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@inproceedings {1699,
	title = {Denoising Of Hyperspectral Images Using Shearlet Transform And Fully Constrained Least Squares Unmixing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1682,
	title = {Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {9},
	year = {2016},
	pages = {2377-2389},
	abstract = {Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images (HSI) is limited, due to sensor limitations. In many applications, HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unmixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from an MSI of the same scene. Endmembers are extracted from the HSI by spectral unmixing, and the exact location of the endmembers is obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a well-posed inverse problem. As a regularizer, we employ sparse coding (SC), for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposed algorithm is applied to real Hyperion and ROSIS datasets. Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI.},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2016.2528339},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1681,
	title = {Hyperspectral Image Compression Optimized for Spectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {pp},
	year = {2016},
	month = {06/2016},
	chapter = {1},
	abstract = {In this paper, we present a new lossy compression method for hyperspectral images that aims to optimally compress in both spatial and spectral domains and simultaneously minimizes the effect of the compression on linear spectral unmixing performance. To achieve this, a nonnegative Tucker decomposition is applied. This decomposition is a function of three dimension parameters. By employing a link between this decomposition and the linear spectral mixing model, an optimization problem is defined to find the optimal parameters by minimizing the root-mean-square error between the abundance matrices of the original and reconstructed data sets. The resulting optimization problem is solved by a particle swarm optimization algorithm. An approximate method for fast estimation of the free parameters is introduced as well. Our simulation results show that, in comparison with well-known state-of-the-art lossy compression methods, an improved compression and spectral unmixing performance of the reconstructed hyperspectral image is obtained. It is noteworthy to mention that the superiority of our method becomes more apparent as the compression ratio grows.},
	issn = {0196-2892},
	doi = {10.1109/TGRS.2016.2574757},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@conference {Kenney2016-ju,
	title = {Lateralisation of the arcuate fasciculus in psychosis \& the role in verbal learning \& auditory verbal hallucinations},
	volume = {26},
	year = {2016},
	pages = {S76{\textendash}S77},
	publisher = {Elsevier Science Bv},
	address = {Po Box 211, 1000 Ae Amsterdam, Netherlands},
	author = {Kenney, J and McInerney, S and McPhilemy, G and Najt, P and Scanlon, C and Arndt, S and Scherz, E and Byrne, F and Leemans, A and Ben Jeurissen and Donohoe, G and Hallahan, B and McDonald, C and Cannon, D}
}
@inproceedings {1697,
	title = {Optical Solutions for Improving Spatial Resolution of Hyperspectral Sensors},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Ashkan  Adibi and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {kenney2016p,
	title = {P. 3.033 Lateralisation of the arcuate fasciculus in psychosis \& the role in verbal learning \& auditory verbal hallucinations},
	journal = {European Neuropsychopharmacology},
	volume = {26},
	year = {2016},
	pages = {S76{\textendash}S77},
	author = {Kenney, J and McInerney, S and McPhilemy, G and Najt, P and Scanlon, C and Arndt, S and Scherz, E and Byrne, F and Alexander Leemans and Ben Jeurissen and G Donohoe and B Hallahan and C McDonald and D Cannon}
}
@inproceedings {1698,
	title = {Superresolution Of Hyperspectral Images Using Spectral Unmixing And Sparse Regularization},
	booktitle = {IGARSS  2016},
	year = {2016},
	month = {07/2016},
	address = {Beijing China},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@conference {1589,
	title = {A 4D CT reconstruction algorithm for fast liquid flow imaging},
	year = {2015},
	abstract = {  The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, ranging from oil extraction to scientific research on fluid dynamics. Current techniques are often limited by a low temporal/spatial resolution. In this talk, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation of a particular voxel in the dynamic region is modeled by a piecewise constant function over time (i.e., the voxel consists of fluid or air). Experiments on simulation data and on a real neutron tomography dataset demonstrate that the proposed approach can significantly increase the temporal resolution in comparison to conventional algorithms.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@article {1570,
	title = {4D-CT reconstruction with unified spatial-temporal patch-based regularization},
	journal = { Inverse Problems and Imaging},
	volume = {9},
	year = {2015},
	pages = {447-467},
	doi = {10.3934/ipi.2015.9.447},
	author = {Kazantsev, Daniil and Thompson, William M. and Lionheart, William R. B. and Van Eyndhoven, Geert and Kaestner,  Anders P. and Katherine J Dobson and Withers, Philip J. and Lee, Peter D.}
}
@article {1541,
	title = {An accurate projection model for diffraction image formation and inversion using a polychromatic cone beam},
	journal = {Journal of Applied Crystallography},
	volume = {48},
	year = {2015},
	pages = {334-343},
	author = {Wim Van Aarle and Ludwig, Wolfgang and King, Andrew and Dayakar Penumadu}
}
@article {1571,
	title = {Band-specific Shearlet-based Hyperspectral Image Noise Reduction},
	journal = {IEEE Transaction Geosciences and Remote Sensing },
	volume = {53},
	number = {13},
	year = {2015},
	month = {03/2015},
	chapter = {1},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1579,
	title = {Employing temporal self-similarity across the entire time domain in computed tomography reconstruction},
	journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences},
	volume = {373},
	number = {2043},
	year = {2015},
	publisher = {The Royal Society},
	abstract = {There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques.},
	isbn = {1471-2962},
	issn = {1364-503X},
	doi = {10.1098/rsta.2014.0389},
	author = {Kazantsev, Daniil and Van Eyndhoven, Geert and Lionheart, William R. B. and Withers, Philip J. and Katherine J Dobson and McDonald, S. A. and Atwood, R and Lee, Peter D.}
}
@conference {1597,
	title = {Gibbs ringing removal in diffusion MRI using second order total variation minimization},
	volume = {23},
	year = {2015},
	pages = {2809},
	author = {Jelle Veraart and Florian Knoll and Jan Sijbers and Els Fieremans and Dmitry S. Novikov}
}
@article {1606,
	title = {An iterative CT reconstruction algorithm for fast fluid flow imaging},
	journal = {IEEE Transactions on Image Processing},
	volume = {24},
	year = {2015},
	pages = {4446-4458},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique.

In this paper, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary.

Quantitative and qualitative results on different simulation experiments and a real neutron tomography dataset show that, in comparison state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, temporal resolution can be substantially increased and thus fluid flow experiments with faster dynamics can be performed.},
	issn = {1057-7149},
	doi = {10.1109/TIP.2015.2466113},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@conference {1588,
	title = {An iterative prior knowledge based reconstruction algorithm for increased temporal/spatial resolution in the CT imaging of fluid flow through solid matter},
	year = {2015},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, e.g., in oil production (the manner in which water displaces oil in rocks determines the fraction of oil that can be recovered) and in scientific research on fluid dynamics (validation of fluid flow models) [1-4]. In this work, a novel iterative computed tomography reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in a twofold manner. Firstly, a dynamic reconstruction is generated assuming the existence of stationary regions (the solid matter) and dynamic regions (the fluid flow) throughout the reconstruction domain. This assumption is enforced by sharing iterative updates in the stationary regions over different time frames. Secondly, the fact that a particular voxel in the dynamic region can typically be described by a piecewise constant (PWC) function over time (i.e., the voxel consists of fluid or air) is exploited by estimating a PWC function in a robust manner in all voxels belonging to the dynamic region at intermediate iterations. The proposed reconstruction algorithm was validated on simulation data and on a real neutron tomography dataset (PSI ICON experiment, courtesy of Manchester X-ray Imaging Facility). These experiments demonstrated that the new iterative technique is able to significantly increase the temporal resolution in comparison to more conventional algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) or Filtered Backprojection (FBP) [5-6].

[1]	V. Cnudde and M. Boone: High-resolution x-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews, vol. 123, pp. 1 {\textendash} 17, 2013
[2]	S. Akin and A. Kovscek: Computed tomography in petroleum engineering research. Geological Society, London, Special Publications, vol. 215, no. 1, pp. 23{\textendash}38, 2003
[3]	O. P. Wennberg, L. Rennan, and R. Basquet: Computed tomography scan imaging of natural open fractures in a porous rock; geometry and fluid flow. Geophysical Prospecting, vol. 57, no. 2, pp. 239{\textendash}249, 2009
[4]	M. Kumar, T. J. Senden, A. P. Sheppard, J. P. Middleton, and M. A. Knackstedt: Visualizing and quantifying the residual phase distribution in core material. Petrophysics, vol. 51, no. 5, p. 323, 2010
[5]	J. Gregor and T. Benson: Computational analysis and improvement of SIRT. IEEE Trans. Med. Imag., vol. 27, no. 7, pp. 918{\textendash}24, 2008
[6]	L. Feldkamp, L. Davis, and J. Kress: Practical cone-beam algorithm. J. Opt. Soc. Am. A, vol. 1, no. 6, pp. 612{\textendash}619, 1984
},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Jan Sijbers}
}
@inproceedings {1622,
	title = {Lossy Compression of Hyperspectral Images Optimizing Spectral Unmixing},
	booktitle = {IGARSS  2015},
	year = {2015},
	address = {Milan, Italy},
	author = {Azam Karami and R.Heylen and Paul Scheunders}
}
@inproceedings {1639,
	title = {Lossy Compression of hyperspectral images optimizing spectral unmixing},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5031-034},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1496,
	title = {Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials},
	journal = {Ultramicroscopy},
	volume = {148},
	year = {2015},
	pages = {10-19},
	abstract = {Electron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm ({\textquotedblleft}POre REconstruction and Segmentation{\textquotedblright}) is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials.},
	doi = {10.1016/j.ultramic.2014.08.008},
	author = {Van Eyndhoven, Geert and Mert Kurttepeli and C. J. {Van Oers} and Pegie Cool and Sara Bals and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {2043,
	title = {SPIE ProceedingsEnergy calibration of photon counting detectors using x-ray tube potential as a reference for material decomposition applications},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging},
	volume = {9412},
	year = {2015},
	pages = {941214},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082979},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082979},
	author = {Das, Mini and Kandel, Bigyan and Park, Chan Soo and Zhihua Liang},
	editor = {Hoeschen, Christoph and Kontos, Despina and Flohr, Thomas G.}
}
@inproceedings {2044,
	title = {SPIE ProceedingsSingle-step, quantitative x-ray differential phase contrast imaging using spectral detection in a coded aperture setup},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging},
	volume = {9412},
	year = {2015},
	pages = {941252},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082977},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082977},
	author = {Das, Mini and Zhihua Liang},
	editor = {Hoeschen, Christoph and Kontos, Despina and Flohr, Thomas G.}
}
@inproceedings {2042,
	title = {SPIE ProceedingsTowards using eye-tracking data to develop visual-search observers for x-ray breast imaging},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment},
	volume = {9416},
	year = {2015},
	pages = {94160V},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082978},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082978},
	author = {Jiang, Zhengqiang and Zhihua Liang and Das, Mini and Gifford, Howard C.},
	editor = {Mello-Thoms, Claudia R. and Kupinski, Matthew A.}
}
@inproceedings {1569,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing},
	year = {2014},
	address = { Lausanne, Suisse},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1532,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@conference {1454,
	title = {Reliable Pore-size Measurements Based on a Procedure Specifically Designed for Electron Tomography Measurements of Nanoporous Samples},
	year = {2014},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Cynthia Van Oers and Mert Kurttepeli and Sara Bals and Pegie Cool and Jan Sijbers}
}
@inproceedings {1521,
	title = {Spectral adaptation of hyperspectral flight lines using VHR contextual information},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International},
	year = {2014},
	pages = {2953-2956},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Quebec City, QC, Canada},
	abstract = {Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.},
	doi = {10.1109/IGARSS.2014.6947096},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6947096},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Pieter Kempeneers and Paul Scheunders}
}
@article {1527,
	title = {A spectral-unmixing approach to estimate water-mass concentrations in case-II waters},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {8},
	year = {2014},
	author = {Dzevdet Burazerovic and Rob Heylen and Dries Raymaekers and Els Knaeps and Paul Scheunders}
}
@proceedings {1907,
	title = {ACIVS 2013, Advanced Concepts for Intelligent Vision Systems},
	volume = {8129},
	year = {2013},
	author = {J Blanc-Talon and A Kazinski and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1392,
	title = {Advances in X-ray diffraction contrast tomography: flexibility in the setup geometry and application to multiphase materials},
	journal = {Journal of Applied Crystallography},
	volume = {46},
	year = {2013},
	month = {04/2013},
	pages = {297 - 311},
	issn = {0021-8898},
	doi = {10.1107/S0021889813002604},
	author = {Reischig, P{\'e}ter and King, Andrew and Nervo, Laura and Vigan{\'o}, Nicola and Guilhem, Yoann and Willem Jan Palenstijn and Kees Joost Batenburg and Preuss, Michael and Ludwig, Wolfgang}
}
@article {1413,
	title = {Reelin Associated With Restricted and Stereotyped Behavior Based on Principal Component Analysis on Autism Diagnostic Interview-Revised},
	journal = {Autism - Open Access},
	volume = {3},
	year = {2013},
	chapter = {107},
	url = {http://www.omicsgroup.org/journals/2165-7890/2165-7890-3-107.pdf},
	author = {Roine, Ulrika and Ripatti, Samuli and Rehnstr{\"o}m, Karola and Timo Roine and Kilpinen, Helena and Surakka, Ida and Wedenoja, Juho and Ylisaukko-oja, Tero and Kempas, Elli and Wessman, Jaana and Moilanen, Irma and Mattila, Marja-Leena and Kielinen, Marko and Jussila, Katja and Suomalainen, Saara and Pulkkinen, Esko and von Wendt, Lennart and Peltonen, Leena}
}
@article {1320,
	title = {Accurate segmentation of dense nanoparticles by partially discrete electron tomography},
	journal = {Ultramicroscopy},
	volume = {114},
	year = {2012},
	pages = {96-105},
	author = {Roelandts, Tom and Kees Joost Batenburg and E. Biermans and C. Kubel and Sara Bals and Jan Sijbers}
}
@article {1459,
	title = {Compression and Noise Reduction of Hyperspectral Images using Tucker Decomposition and Discrete Wavelet Transform},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {5},
	year = {2012},
	chapter = {444},
	author = {Azam Karami and M.Yazdi and G. Mercier}
}
@conference {1325,
	title = {Correction of Gibbs ringing in diffusion MRI data using total variation regularization},
	year = {2012},
	month = {January},
	pages = {99},
	keywords = {MRI distortion artefacts},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@proceedings {1463,
	title = {Hyperspectral Image Compression Using 3d Discrete Cosine Transform and Support Vector Machine Learning},
	year = {2012},
	address = {Montreal, Canada},
	author = {Azam Karami and S. Beheshti and M.Yazdi}
}
@article {1349,
	title = {Identification and characterization of Huntington related pathology: an in vivo DKI imaging study},
	journal = {NeuroImage},
	volume = {63},
	year = {2012},
	month = {09/2012},
	pages = {653-662},
	doi = {http://dx.doi.org/10.1016/j.neuroimage.2012.06.032},
	author = {Ines Blockx and Marleen Verhoye and Johan Van Audekerke and Irene  Bergwerf and Jack X Kane and Rafael  Delgado Y Palacios and Jelle Veraart and Ben Jeurissen and Kerstin Raber and Von H{\"o}rsten, Stephan and Peter Ponsaerts and Jan Sijbers and Trygve B Leergaard and Annemie Van Der Linden}
}
@article {1516,
	title = {Improved sensitivity to cerebral white matter abnormalities in Alzheimer{\textquoteright}s disease with spherical deconvolution based tractography.},
	journal = {PloS one},
	volume = {7},
	year = {2012},
	month = {2012},
	pages = {e44074},
	abstract = {Diffusion tensor imaging (DTI) based fiber tractography (FT) is the most popular approach for investigating white matter tracts in vivo, despite its inability to reconstruct fiber pathways in regions with "crossing fibers." Recently, constrained spherical deconvolution (CSD) has been developed to mitigate the adverse effects of "crossing fibers" on DTI based FT. Notwithstanding the methodological benefit, the clinical relevance of CSD based FT for the assessment of white matter abnormalities remains unclear. In this work, we evaluated the applicability of a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in 25 patients with early Alzheimer{\textquoteright}s disease. Both CSD and DTI based FT were used to reconstruct two white matter tracts: one with regions of "crossing fibers," i.e., the superior longitudinal fasciculus (SLF) and one which contains only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). The DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD), obtained from these tracts were related to memory function. Our results show that in the tract with "crossing fibers" the relation between FA/MD and memory was stronger with CSD than with DTI based FT. By contrast, in the fiber bundle where one fiber population predominates, the relation between FA/MD and memory was comparable between both tractography methods. Importantly, these associations were most pronounced after adjustment for the planar diffusion coefficient, a measure reflecting the degree of fiber organization complexity. These findings indicate that compared to conventionally applied DTI based FT, CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture.},
	keywords = {Aged, 80 and over, Alzheimer Disease, Cerebrum, Cognition, Corpus Callosum, diffusion tensor imaging, Female, Humans, Male, Memory, Nerve Fibers, Myelinated},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0044074},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@proceedings {1235,
	title = {ACIVS 2011, Advanced Concepts for Intelligent Vision Systems},
	volume = {6915},
	year = {2011},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and R. Kleihorst and Paul Scheunders}
}
@conference {1549,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	volume = {5},
	year = {2011},
	address = {Lille, France},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1551,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	year = {2011},
	address = {Qu{\'e}bec, Canada},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1552,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	year = {2011},
	address = {Paris, France},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {Kudzinava_2011_ESMRMB,
	title = {Denoising of DKI images: effect on feasibility and accuracy of kurtosis parameters},
	year = {2011},
	month = {October},
	pages = {236},
	address = {Leipzig, Germany},
	abstract = {Diffusion Kurtosis Imaging (DKI) is a new MRI technique, which quantifies non-Gaussianity of water diffusion. DKI requires high b-values, resulting in a low SNR of acquired diffusion weighted images (DWIs). Generally, kurtosis tensors are estimated from these DWIs using Weighted Least Squares (WLS) or Maximum Likelihood (ML) methods. In this work, we apply the Restricted Local Maximum Likelihood (RLML) denoising algorithm to remove noise from DWIs prior to WLS estimation. Then, we compare the RLML+WLS approach to the WLS and ML methods in terms of feasibility, that is the percentage of estimated tensors that satisfy certain DKI model constraints; and in terms of accuracy of calculated values of Mean Kurtosis.},
	keywords = {denoising, diffusion kurtosis imaging, maximum likelihood estimation},
	author = {Maryna Kudzinava and Jeny Rajan and Jan Sijbers}
}
@conference {Kudzinava_2011_WMSG,
	title = {Diffusion and kurtosis parameters in white matter of premature newborns},
	year = {2011},
	month = {August},
	pages = {21},
	address = {Reykjavik, Iceland},
	abstract = {Diffusion Tensor Imaging (DTI) is often used to study brain maturation processes and white matter (WM) injuries in premature newborns. However, DTI assumes that water diffusion in biological tissues is free. In reality, there is always structural hindrance in WM, which only increases with age due to myelination. Diffusional Kurtosis Imaging (DKI) relaxes the assumption of free diffusion and allows estimation of conventional diffusion parameters plus new kurtosis measures, which are related to tissue complexity. In this work, we present and discuss the first statistics of DKI parameters in WM of two prematurely born infants.},
	keywords = {anisotropy, diffusion, kurtosis, premature newborns, white matter},
	author = {Maryna Kudzinava and A. Plaisier and J. Dudink and Jan Sijbers}
}
@conference {R.jveraartG.H.mverhoyeP.jsijbersavdlinde2011,
	title = {Diffusion kurtosis abnormalities in pre-symptomatic (alpha)-synycleunopathy mouse model},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Rafael  Delgado Y Palacios and Jelle Veraart and Greetje Vanhoutte and H. Schell and Marleen Verhoye and P. Kahle and Jan Sijbers and Annemie Van Der Linden}
}
@conference {1550,
	title = {Diffusion tensor imaging and cognition in Alzheimer{\textquoteright}s disease: the influence of crossing fibers},
	year = {2011},
	address = {Lunteren, The Netherlands},
	author = {Wielaard, Ilse and Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1307,
	title = {Distortion correction of DKI data: affine approach},
	year = {2011},
	month = {December},
	abstract = {In this work, the performance of FMRIB{\textquoteright}s Linear Registration Tool (FLIRT) is evaluated with respect to motion and distortion correction of diffusion kurtosis imaging (DKI) data. FLIRT is tested on two DKI datasets heavily distorted by motion and eddy currents artefacts. Six different criteria are used for evaluation. Our results show that distortion correction using FLIRT substantially improves the original data. However, being an affine registration tool, FLIRT cannot cope with the amount of non-linear distortion present in DKI datasets. Hence, we conclude that, for correction of DKI data, non-linear registration techniques are more suitable.},
	keywords = {medical imaging},
	author = {Maryna Kudzinava and Alexander Leemans and Wilfried Philips and Daniele Perrone and Jan Aelterman and J. Dudink and Jan Sijbers}
}
@conference {1308,
	title = {Gibbs artifact suppression for DT-MRI data},
	year = {2011},
	month = {December},
	keywords = {medical imaging},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@proceedings {1464,
	title = {Hyperspectral Image Compression based on Tucker Decomposition and Wavelet Transform},
	year = {2011},
	address = {Lisbon, Portugal},
	author = {Azam Karami and M.Yazdi and G. Mercier}
}
@article {R.AdriaanKimmverhoyedpootJoukeJ.HjsijbersOavdlinde2011,
	title = {Magnetic resonance imaging and spectroscopy reveal differential hippocampal changes in anhedonic and resilient subtypes of the chronic mild stress rat model},
	journal = {Biological psychiatry},
	volume = {70},
	number = {5},
	year = {2011},
	pages = {449-457},
	author = {Rafael  Delgado Y Palacios and Campo Adriaan and Henningsen Kim and Marleen Verhoye and Dirk H J Poot and Dijkstra Jouke and Johan Van Audekerke and H Benveniste and Jan Sijbers and O. Wiborg and Annemie Van Der Linden}
}
@article {1458,
	title = {Noise Reduction of Hyperspectral Images Using Kernel Nonnegative Tucker Decomposition},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {5},
	year = {2011},
	chapter = {487},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@inproceedings {Kudzinava_2011_ISBI,
	title = {Optimized Workflow for Diffusion Kurtosis Imaging of Newborns},
	booktitle = {ISBI, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2011},
	month = {April},
	pages = {922-926},
	address = {Chicago, USA},
	abstract = {Diffusional kurtosis imaging (DKI) is a recently proposed extension of the conventional DTI model. It has been shown to offer more sensitive characterization of neural tissues than DTI. So far, DKI has only been applied to adult human and small animal studies, but not yet to human newborns. In this work, we present an optimized workflow for the acquisition and processing of DKI images of newborns. First, optimal set of diffusion weighting gradients for DKI studies of newborn subjects is proposed. Optimized gradients allow to estimate DKI parameters with the highest precision. Next, preprocessing and segmentation of the DKI data is considered, including motion correction, eddy currents suppression, intensity modulation and gradients reorientation. Finally, statistics of estimated diffusion and kurtosis parameters for different neonatal brain tissues are calculated.},
	keywords = {diffusion and kurtosis parameters, DKI, motion  correction, newborns, optimal gradients, precision},
	doi = {10.1109/ISBI.2011.5872554},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5872554},
	author = {Maryna Kudzinava and Dirk H J Poot and A. Plaisier and Jan Sijbers}
}
@article {1269,
	title = {Successive Cambia: A Developmental Oddity or an Adaptive Structure?},
	journal = {PLoS ONE},
	volume = {6},
	year = {2011},
	month = {1/2011},
	pages = {e16558},
	doi = {10.1371/journal.pone.0016558},
	author = {Robert, Elisabeth M. R. and Schmitz, Nele and Boeren, Ilse and Driessens, Tess and Herremans, Kristof and Johan De Mey and Elke Van de Casteele and Beeckman, Hans and Koedam, Nico},
	editor = {Peer, Wendy}
}
@inproceedings {1414,
	title = {Training Simulator for Flotation Process Operators},
	booktitle = {18th IFAC World Congress},
	year = {2011},
	url = {http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/2171.pdf},
	author = {Timo Roine and Kaartinen, Jani and Lamberg, Pertti}
}
@proceedings {1466,
	title = {Best Rank-r Tensor Selection Using Genetic Algorithm for Better Noise Reduction and Compression of Hyperspectral Images},
	year = {2010},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@article {1460,
	title = {Compression and Noise Reduction of Hyperspectral Images Using Hybrid Genetic Algorithm and Nonnegative Tucker Decomposition},
	journal = {International Journal of Information studies },
	volume = {2},
	number = {10},
	year = {2010},
	chapter = {207},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@article {jbatenbujsijbersH.F.E.2010,
	title = {DART: A Robust Algorithm for Fast Reconstruction of 3D Grain Maps},
	journal = {Journal of Applied Crystallography},
	volume = {43},
	year = {2010},
	pages = {1464-1473},
	doi = {https://doi.org/10.1107/S0021889810034114},
	author = {Kees Joost Batenburg and Jan Sijbers and H.F. Poulsen and E. Knudsen}
}
@proceedings {1465,
	title = {Hyperspectral Image Compression Based on Tucker Decomposition and Discrete Cosine Transform},
	year = {2010},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@inproceedings {Haest10,
	title = {An object-based approach to quantity and quality assessment of heathland habitats in the framework of NATURA 2000 using hyperspectral airborne AHS images.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Wards clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93\% (Kappa= 0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodologys potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.},
	keywords = {Application, Classification, Contextual, Ecosystem, Hyper spectral, Landscape, Object, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Haest_211_An_object-based_approach_to_quantity_and_quality_assessment_of_heath_land_habitats.pdf},
	author = {Haest, B. and Guy Thoonen and Jeroen Vanden Borre and Toon Spanhove and Stephanie Delalieux and L. Bertels and Kooistra, L. and C A M{\"u}cher and Paul Scheunders}
}
@article {jbatenbuBalsjsijbersKubelMidgleyHernandezKaiserEncinaCoronadoTendeloo2009,
	title = {3D imaging of nanomaterials by discrete tomography},
	journal = {Ultramicroscopy},
	volume = {109},
	year = {2009},
	pages = {730-740},
	doi = {10.1016/j.ultramic.2009.01.009},
	author = {Kees Joost Batenburg and Sara Bals and Jan Sijbers and C. Kubel and P.A. Midgley and J.C. Hernandez and U. Kaiser and E.R. Encina and E.A. Coronado and Van Tendeloo, Gustaaf}
}
@inproceedings {jrajanbjeurissjsijbersK.2009,
	title = {Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion},
	booktitle = {13th International Machine Vision and Image Processing Conference},
	year = {2009},
	pages = {123-127},
	address = {Dublin, Ireland},
	author = {Jeny Rajan and Ben Jeurissen and Jan Sijbers and Keizer Kannan}
}
@inproceedings {1417,
	title = {Development of a Machine Vision System to Monitor a Grinding Mill Prototype},
	booktitle = {12th European Symposium on Comminution and Classification (ESCC 2009)},
	year = {2009},
	author = {Timo Roine and Pietil{\"a}, Janne and Kaartinen, Jani and Blanz, Peter and Rantala, Pertti}
}
@article {PostnovSchutterjsijbersKarperienClerck2009,
	title = {Glucocorticoid-Induced Osteoporosis in Growing Mice Is Not Prevented by Simultaneous Intermittent PTH Treatment},
	journal = {Calcified Tissue International},
	volume = {85},
	number = {6},
	year = {2009},
	month = {December},
	pages = {530-537},
	doi = {10.1007/s00223-009-9301-3},
	author = {A. Postnov and T. De Schutter and Jan Sijbers and M. Karperien and N. De Clerck}
}
@inproceedings {1416,
	title = {Machine Vision of Flotation Froths with a Rapid-Prototyping Platform},
	booktitle = {IFAC Workshop on Automation in Mining, Mineral and Metal Industry (IFACMMM2009)},
	year = {2009},
	author = {Kaartinen, Jani and H{\"a}t{\"o}nen, Jari and Timo Roine}
}
@article {KempeneersZarco-TejadaNorthsdbackerDelalieuxSepulcre-CantoMoralesAardtSagardoyCoppinpscheund2008,
	title = {Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery},
	journal = {International Journal of Remote Sensing},
	volume = {29},
	number = {17},
	year = {2008},
	pages = {5093-5111},
	author = {Pieter Kempeneers and P.J. Zarco-Tejada and P.R.J. North and Steve De Backer and Stephanie Delalieux and G. Sepulcre-Canto and F. Morales and Jan van Aardt and Ruth Sagardoy and P. Coppin and Paul Scheunders}
}
@inproceedings {Thoonen08,
	title = {Spatial Classification of Hyperspectral Data of Dune Vegetation along the Belgian Coast},
	booktitle = {Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International},
	volume = {3},
	year = {2008},
	month = {July},
	pages = {III-483 - III-486},
	address = {Boston, MA, USA},
	abstract = {This work evaluates a classification method, including spatial information, for dune vegetation along the Belgian coastline. The used method is a recursive supervised segmentation algorithm based on a tree-structured Markov Random Field. This technique describes a K-ary field as a sequence of binary Markov Random Fields, each of which is represented by a node in the tree. The obtained classification results were compared to results with the same data set, for a purely spectral classification and a spectral classification, followed by spatial smoothing.},
	keywords = {airborne data, airborne radar, Belgian Coast, binary Markov Random Field, dune environment, dune vegetation mapping, Europe, hyperspectral data, image classification, image segmentation, Markov processes, spatial classification, spatial smoothing, supervised segmentation algorithm, tree-structured Markov Random Field, trees (mathematics), TS-MRF model, vegetation mapping},
	isbn = {978-1-4244-2807-6},
	doi = {10.1109/IGARSS.2008.4779389},
	author = {Guy Thoonen and Steve De Backer and S. Provoost and Pieter Kempeneers and Paul Scheunders}
}
@mastersthesis {1345,
	title = {Information Extraction from Hyperspectral Images Applied to Vegetation},
	volume = {doctor in physics},
	year = {2007},
	type = {PhD thesis},
	author = {Pieter Kempeneers}
}
@article {1462,
	title = {Voltage Security Enhancement and Congestion Management via STATCOM \& IPFC Using Artificial Intelligence},
	journal = {Iranian Journal of Science and Technology},
	volume = {1},
	number = {12},
	year = {2007},
	chapter = {289},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1468,
	title = {Application of RGA to Optimal Choice and Allocation of UPFC for Voltage Security Enhancement in Deregulated Power System},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {KempeneerssdbackerZarco-TejadaDelalieuxSepulcre-Cant{\'o}MoralesSagardoyAardtCoppinpscheund2006,
	title = {Chlorophyll retrieval from canopy reflectance over orchards using hyperspectral techniques},
	booktitle = {2nd International Symposium on Recent Advances in Quantitative Remote Sensing, 25-29 September 2006, Torrent (Valencia), Spain},
	year = {2006},
	author = {Pieter Kempeneers and Steve De Backer and P.J. Zarco-Tejada and Stephanie Delalieux and G. Sepulcre-Cant{\'o} and F. Morales and Ruth Sagardoy and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@article {1461,
	title = {Coordination of UPFC \& SVC for Voltage Security Enhancement},
	journal = {WSEAS Transactions on Circuits and Systems},
	volume = {5},
	number = {6},
	year = {2006},
	chapter = {740},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1467,
	title = {Optimal Location of STATCOM for Voltage Security Enhancement via Artificial Intelligent},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {KempeneerssdbackerTejadaDelalieuxCantoIribasAardtCoppinpscheund2006,
	title = {Stress detection in orchards with hyperspectral remote sensing data},
	booktitle = {SPIE Remote Sensing, Stockholm, Sweden, september 11-14},
	volume = {6359},
	number = {38},
	year = {2006},
	month = {September},
	author = {Pieter Kempeneers and Steve De Backer and P J Zarco Tejada and Stephanie Delalieux and G Sepulcre Canto and F Morales Iribas and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@proceedings {1469,
	title = {Voltage Security Enhancement by Optimal FACTS Location via RGA},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@article {sdbackerKempeneersDebruynpscheund2005,
	title = {A Band Selection Technique for Spectral Classification},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {2},
	number = {3},
	year = {2005},
	month = {July},
	pages = {319-323},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders}
}
@inproceedings {jjuntujsijbersdvandyckGielen2005,
	title = {Bias Field Correction for MRI Images},
	booktitle = {Proceedings of the 4th International Conference on Computer Recognition Systems (CORES05)},
	series = {Advances in Computer Science},
	year = {2005},
	month = {May},
	pages = {543-551},
	publisher = {Springer},
	organization = {Springer},
	address = {Rydzyna Castle, Poland},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck and Jan L Gielen},
	editor = {Marek Kurzynsji and Edward Puchala and Michal Wozniak and Andrzej Zolnierek}
}
@article {KempeneerssdbackerDebruynpscheund2005,
	title = {Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {43},
	year = {2005},
	month = {March},
	pages = {610-614},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and Paul Scheunders}
}
@inproceedings {KempeneerssdbackerDebruynProvoostpscheund2005,
	title = {Hyperspectral classification applied to the Belgian coastline},
	booktitle = {SPIE Remote Sensing, Bruges, Belgium, 19-22 september},
	volume = {5982},
	number = {15},
	year = {2005},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and S. Provoost and Paul Scheunders}
}
@inproceedings {KempeneersSterckxDebruynParkRuddicksdbackerpscheund2005,
	title = {Retrieval of oceanic constituents from ocean color using simulated annealing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005, Seoul, Korea, 25-29 July},
	volume = {8},
	year = {2005},
	pages = {5651-5654},
	author = {Pieter Kempeneers and S Sterckx and W. Debruyn and Y. Park and K. Ruddick and Steve De Backer and Paul Scheunders}
}
@inproceedings {sdbackerKempeneersDebruynpscheund2004,
	title = {Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images},
	booktitle = {Image Analysis and Recognition Proc. of International Conference on Image Analysis and Recognition},
	series = {Lecture Notes in Computer Science},
	year = {2004},
	pages = {497-503},
	publisher = {Springer},
	organization = {Springer},
	address = {Porto, Portugal},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders},
	editor = {A. Campilho and M. Kamel}
}
@inproceedings {KempeneerssdbackerDerondeBertelsDebruynpscheund2004,
	title = {Classifying hyperspectral airborne imagery for vegetation survey along coastlines},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Anchorage, USA, 20-24 september},
	volume = {2},
	year = {2004},
	pages = {1475-1478},
	publisher = {IEEE International},
	organization = {IEEE International},
	address = {Anchorage, USA},
	author = {Pieter Kempeneers and Steve De Backer and B. Deronde and L. Bertels and W. Debruyn and Paul Scheunders}
}
@inproceedings {KempeneerssdbackerDelalieuxSterckxDebruynCoppinpscheund2004,
	title = {Upscaling of spectroradiometer data for stress detection in orchards with remote sensing},
	booktitle = {SPIE Remote Sensing, Gran Canaria, 13-16 september},
	volume = {127},
	year = {2004},
	pages = {37-45},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Maspalomas, Spain},
	author = {Pieter Kempeneers and Steve De Backer and Stephanie Delalieux and S Sterckx and W. Debruyn and P. Coppin and Paul Scheunders},
	editor = {Manfred Owe, Guido D{\textquoteright}Urso, Ben T. Gouweleeuw, Anne M. Jochum}
}
@inproceedings {sdbackerKempeneersDebruynpscheund2004,
	title = {Wavelet Based Hyperspectral Data Analysis for Vegetation Stress Classification},
	booktitle = {Proc. of Advanced Concepts for Intelligent Vision Systems},
	year = {2004},
	pages = {387-391},
	address = {Brussels, Belgium},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders}
}
@inproceedings {SterckxCoppinsdbackerDebruynKempeneersMeulemanNackaertsReusenpscheund2003,
	title = {Information extraction techniques for monitoring of stress symptoms in orchards},
	booktitle = {Proc. Earsel 2003, Imaging Spectroscopy workshop Oberpfaffenhofen},
	year = {2003},
	pages = {278-283},
	author = {S Sterckx and P. Coppin and Steve De Backer and W. Debruyn and Pieter Kempeneers and K. Meuleman and K. Nackaerts and I. Reusen and Paul Scheunders}
}
@inproceedings {KempeneerssdbackerDebruynpscheund2003,
	title = {Wavelet Based Feature Extraction for Hyperspectral Vegetation Monitoring},
	booktitle = {SPIE International Symposium on Remote Sensing, Image and Signal Processing for Remote Sensing IX},
	year = {2003},
	pages = {297-305},
	address = {Barcelona, Spain},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and Paul Scheunders},
	editor = {L. Bruzzone}
}
@inproceedings {mverhoyejsijbersFransenKooySorianoWillemsavdlinde1999,
	title = {In vivo neuro MRI microscopy of L1 knockout mice demonstrated similarities with human crash syndrome},
	booktitle = {Belgian Society for Neuroscience},
	year = {1999},
	month = {May},
	address = {Brussels},
	author = {Marleen Verhoye and Jan Sijbers and Erik Fransen and R.F. Kooy and P. Soriano and P. J. Willems and Annemie Van Der Linden}
}
@article {KooyReyniersmverhoyejsijbersCrasOostraWillemsavdlinde1999,
	title = {Neuroanatomy of the fragile X knockout mouse brain studied using in vivo high resolution Magnetic Resonance Imaging (MRI)},
	journal = {European Journal of Human Genetics},
	volume = {7},
	year = {1999},
	pages = {526-532},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and P. Cras and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@article {WeyngvdwouweKoprowskiDaeleDhaenepscheundJacobMarck1999,
	title = {Value of morphometry, texture analysis, densitometry and histometry in the differential diagnosis and prognosis of malignant mesothelioma},
	journal = {Journal of Pathology},
	volume = {4},
	number = {189},
	year = {1999},
	pages = {581-589},
	author = {Barbara Weyn and G. Van de Wouwer and M. Koprowski and A. Van Daele and K. Dhaene and Paul Scheunders and W. Jacob and E. Van Marck}
}
@inproceedings {dmeersmpscheunddvandyck1998,
	title = {Classification of microcalcifications using texture-based features},
	booktitle = {Digital Mammography},
	year = {1998},
	pages = {233-236},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck},
	editor = {N Karssemeijer and L. Van Erning}
}
@inproceedings {mverhoyejsijbersKooyReyniersFransenOostraWillemsavdlinde1998,
	title = {In vivo assessment of cerebellum volume in transgenic Fragile X Knockout mice using MRI microscopy at 7T},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {1998},
	month = {April},
	address = {Sydney, Australia},
	author = {Marleen Verhoye and Jan Sijbers and R.F. Kooy and E. Reyniers and Erik Fransen and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {mverhoyejsijbersFransenKooySorianoWillemsavdlinde1998,
	title = {In vivo neuro MRI micrsocopy of L1 Knockout mice demonstrated similarities with human CRASH syndrome},
	booktitle = {Sixth Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine},
	number = {1371},
	year = {1998},
	month = {April},
	address = {Sydney, Australia},
	author = {Marleen Verhoye and Jan Sijbers and Erik Fransen and R.F. Kooy and P. Soriano and P. J. Willems and Annemie Van Der Linden}
}
@article {FransenDhoogheCampmverhoyejsijbersReyniersSorianoKamiguchiWillemsenKoekoekZeeuwDeynavdlindeLemmonKooyWillems1998,
	title = {L1 knockout mice show dilated ventricles, vermis hypoplasia and impaired exploration patterns},
	journal = {Human Molecular Genetics},
	volume = {7},
	number = {6},
	year = {1998},
	pages = {999-1009},
	doi = {10.1093/hmg/7.6.999},
	author = {Erik Fransen and R. Dhooghe and G. Van Camp and Marleen Verhoye and Jan Sijbers and E. Reyniers and P. Soriano and H. Kamiguchi and R. Willemsen and K.E. Koekoek and C.I. De Zeeuw and P.P. De Deyn and Annemie Van Der Linden and V. Lemmon and R.F. Kooy and P. J. Willems}
}
@inproceedings {mverhoyejsijbersKooyReyniersFransenOostraWillemsavdlinde1998,
	title = {MRI as a tool to study brain structure from mouse models of mental retardation},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3337},
	year = {1998},
	month = {February},
	pages = {314-320},
	address = {San Diego, CA, USA},
	author = {Marleen Verhoye and Jan Sijbers and R.F. Kooy and E. Reyniers and Erik Fransen and B. A. Oostra and P. J. Willems and Annemie Van Der Linden},
	editor = {Eric A. Hoffman}
}
@inproceedings {KooyReyniersmverhoyejsijbersOostraWillemsavdlinde1998,
	title = {Neuroanatomy of the fragile X knockout mouse brain studied using high-resolution MRI},
	booktitle = {48th Annual Meeting of the American Society of Human Genetics},
	year = {1998},
	month = {October},
	pages = {1918},
	address = {Denver, CO (USA)},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {KooyReyniersDHoogheDeynavdlindemverhoyejsijbersOostraWillems1997,
	title = {Characterization of the Fragile X knockout mouse},
	booktitle = {Abstracts of the second annual meeting of the Belgian society of neuroscience},
	year = {1997},
	month = {May},
	address = {Brussels, Belgium},
	author = {R.F. Kooy and E. Reyniers and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers and B. A. Oostra and P. J. Willems}
}
@inproceedings {mverhoyejsijberspscheunddvandyckReyniersKooyWillemsCrasOostraavdlinde1997,
	title = {In vivo assessment of cerebellum volume in transgenic fragile X knockout mice using MTI microscopy at 7T},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology: 14th annual meeting},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {45-46},
	address = {Brussels, Belgium},
	author = {Marleen Verhoye and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra and Annemie Van Der Linden}
}
@inproceedings {KooyReyniersmverhoyejsijbersFransenCampCrasOostraavdlindeWillems1997,
	title = {MRI as a tool to study brain structure from mouse models of mental retardation},
	booktitle = {Abstracts of the 8th international workshop on Fragile X syndrome and X-linked mental retardation},
	year = {1997},
	month = {August},
	address = {Picton, Ontario, Canada},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and Erik Fransen and C. Van Camp and P. Cras and B. A. Oostra and Annemie Van Der Linden and P. J. Willems}
}
@inproceedings {mverhoyeavdlindejsijberspscheunddvandyckReyniersKooyWillemsCrasOostra1996,
	title = {High resolution MRI study of the cerebellum of mice as a function of age, in a mouse model for fragile X mental retardation},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1996},
	month = {September},
	pages = {168},
	address = {Prague, Czech Republic},
	author = {Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra}
}
