@article {2453,
	title = {Longitudinal brain-age predictions comprising long-duration spaceflight missions},
	journal = {npj Microgravity},
	volume = {12},
	year = {2026},
	month = {Jan-12-2026},
	doi = {10.1038/s41526-026-00575-3},
	url = {https://www.nature.com/articles/s41526-026-00575-3https://www.nature.com/articles/s41526-026-00575-3.pdfhttps://www.nature.com/articles/s41526-026-00575-3https://www.nature.com/articles/s41526-026-00575-3.pdf},
	author = {Tang, Ge and Patil, Kaustubh R. and Hoffstaedter, Felix and More, Shammi and Eickhoff, Simon B. and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Gerlach, Darius and Nosikova, Inna and Riabova, Alexandra and Pechenkova, Ekaterina and Petrovichev, Viktor and Rukavishnikov, Ilya and Makovskaya, Lyudmila and Ombergen, Angelique Van and Wuyts, Floris L. and Eulenburg, Peter zu}
}
@article {2431,
	title = {Physiological cerebrospinal fluid interactions between brain and eye structures are altered after long-duration spaceflight},
	journal = {Experimental Physiology},
	year = {2026},
	doi = {http://doi.org/10.1113/EP093112},
	author = {Ge Tang and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Inna Nosikova and Alexandra Riabova and Ekaterina V. Pechenkova and Petrovichev, Victor and Ilya Rukavishnikov and Stefan Sunaert and Paul M Parizel and Lyudmila Makovskaya and Valentin Sinitsyn and Jan Sijbers and Jitka Annen and Steven S L Laureys and Angelique Van Ombergen and Seyed-Ahmad Ahmadi and Floris L Wuyts and Peter zu Eulenburg}
}
@article {2439,
	title = {Spatio-temporal characterization of gastric distensibility in upper endoscopy identifies the presence of Helicobacter pylori},
	journal = {IEEE Transactions on Medical Imaging},
	year = {2026},
	pages = {1-1},
	abstract = {Assessment of gastric distensibility is crucial for understanding various gastrointestinal disorders, particularly those affecting gastric motility and reflux conditions. Gastric distensibility can be altered by pathological conditions such as linitis plastica or gastric mucosal inflammation, as seen in Helicobacter pylori infections. Although distensibility is evaluated during routine endoscopy, the assessment remains purely visual. Moreover, some specialized endoscopic tools assess only specific gastric regions and are often unavailable. Artificial intelligence tools may used to estimate the gastric three-dimensional surface from the endoscopy video and capture temporal distensibility changes of the gastric surface. This paper introduces a novel spatio-temporal characterization of the gastric distensibility during a routine endoscopy procedure. The method reconstructs the gastric surface for each frame of a short endoscopy video using a shape-from- shading network trained with a synthetic endoscopy collection. Local and regional changes in surface bending are then quantified, which herein depend on the distensibility. This spatio-temporal characterization was applied to a group of 160 patients, among them 60 diagnosed with Helicobacter pylori confirmed by pathology examination. The obtained characteristics in three different regions: body, antrum, and pylorus, demonstrated significant differences between patients with and without Helicobacter pylori under a t-test (p < 0.05). The proposed method effectively captures subtle gastric wall bending from routine endoscopy videos, providing a strategy for analyzing distensibility in pathological conditions across various gastric regions without requiring specialized tools.},
	keywords = {Bending, Cameras, Endoscopes, Endoscopy, Gastric distensibility, Gastroenterology, Helicobacter pylori, image reconstruction, Protocols, Spatio-temporal characterization, Stomach, Surface reconstruction, Three-dimensional displays, Three-dimensional reconstruction, Videos},
	doi = {10.1109/TMI.2026.3672198},
	author = {Ruano-Balseca, Josu{\'e} and Bravo, Diego and Diana Giraldo and G{\'o}mez, Mart{\'\i}n and Gonzalez, Fabio A. and Romero, Eduardo}
}
@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.}
}
@book {2458,
	title = {Handbook of Diffusion MR Tractography: Multishell models},
	year = {2025},
	pages = {201 - 220},
	publisher = {Elsevier},
	organization = {Elsevier},
	isbn = {9780128188941},
	doi = {10.1016/B978-0-12-818894-1.00008-2},
	author = {Morez, Jan and De Luca, Alberto and Pietsch, Maximillian and Christiaens, Daan and Baete, Steven H. and Reisert, Marco and Ben Jeurissen}
}
@article {2454,
	title = {MReye-Seg: development and validation of an automated MRI pipeline for standardised ocular and orbital morphometrics},
	journal = {Eye},
	volume = {39},
	year = {2025},
	month = {Jan-12-2025},
	pages = {3294 - 3305},
	issn = {0950-222X},
	doi = {10.1038/s41433-025-04044-1},
	url = {https://www.nature.com/articles/s41433-025-04044-1https://www.nature.com/articles/s41433-025-04044-1.pdfhttps://www.nature.com/articles/s41433-025-04044-1.pdfhttps://www.nature.com/articles/s41433-025-04044-1},
	author = {Tang, Ge and Ahmadi, Seyed-Ahmad and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Nosikova, Inna and Ryabova, Alexandra and Pechenkova, Ekaterina and Petrovichev, Viktor and Rukavishnikov, Ilya and Velten, Theresa and Gripshi, Marina and Van Ombergen, Angelique and Wuyts, Floris L. and zu Eulenburg, Peter}
}
@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 {2323,
	title = {CAD-ASTRA: A versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {3425-3439},
	doi = {https://doi.org/10.1364/OE.498194},
	author = {Pavel Paramonov and Nicholas Francken and Jens Renders and Domenico Iuso and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@article {2360,
	title = {Direct reconstruction of wet foam from sparse-view, dynamic X-ray CT scans},
	journal = {Advanced Engineering Materials},
	volume = {2400957},
	year = {2024},
	pages = {1-10},
	doi = {https://doi.org/10.1002/adem.202400957},
	author = {Jens Renders and Lucia Mancini and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2440,
	title = {Estimating Polyp Size From a Single Colonoscopy Image Using a Shape-From-Shading Model},
	booktitle = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2024},
	pages = {1-5},
	keywords = {Accuracy, Colonoscopy, Depth estimation, image segmentation, polyp size estimation, Solid modeling, Surface reconstruction, Surveillance, Synthetic database, Three-dimensional displays},
	doi = {10.1109/ISBI56570.2024.10635358},
	author = {Ruano-Balseca, Josu{\'e} and Bravo, Diego and Diana Giraldo and G{\'o}mez, Mart{\'\i}n and Gonzalez, Fabio A. and Manzanera, Antoine and Romero, Eduardo}
}
@conference {2460,
	title = {Fixel-based analysis of multi-shell diffusion MRI reveals white matter changes after long-duration spaceflight},
	year = {2024},
	pages = {2057},
	author = {Ben Jeurissen and Steven Jillings and Giraldo, Diana L and Van Ombergen, Angelique and Tomilovskaya, Elena and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Petrovichev, Victor and Sijbers, Jan and zu Eulenburg, Peter and Wuyts, Floris L}
}
@article {2347,
	title = {Imaging Algorithm and Optimal Measurement Protocol for 3D Impedance Tomography on 2D High Density Micro Electrode Array},
	journal = {IEEE Sensors},
	volume = {24},
	year = {2024},
	pages = {24452-24465},
	doi = {10.1109/JSEN.2024.3417289},
	author = {Jian Deng and Jens Renders and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@mastersthesis {2363,
	title = {Improved 4DCT reconstruction algorithms for the imaging of foam microstructure formation},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Jens Renders}
}
@article {2321,
	title = {Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {1135-1150},
	doi = {https://doi.org/10.1364/OE.502542},
	author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@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}
}
@conference {2337,
	title = {Reconstruction with rigid motion correction technique in CT imaging: A simulation and application study},
	year = {2024},
	address = {Vietnam Institute for Advanced Study in Mathematics (VIASM), Hanoi, Vietnam},
	author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@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}
}
@inproceedings {2353,
	title = {Synthesizing fractional anisotropy maps from T1-weighted magnetic resonance images using a simplified generative adversarial network},
	booktitle = {Medical Imaging 2024: Clinical and Biomedical Imaging},
	volume = {12930},
	year = {2024},
	pages = {129302P},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	keywords = {Diffusion weighted imaging, Fractional anisotropy, Generative adversarial networks, Image-to-image translation, MRI synthesis, Structural MRI},
	doi = {10.1117/12.3009706},
	url = {https://doi.org/10.1117/12.3009706},
	author = {Murcia-Tapias, Alyhuwert and Diana Giraldo and Romero, Eduardo}
}
@inproceedings {2373,
	title = {X-ray image reconstruction for continuous acquisition with accelerated rotation},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3027622},
	author = {Ben Huyge and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2365,
	title = {X-ray image reconstruction for continuous acquisitions with a generalized motion model},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {39192-39207},
	doi = {https://doi.org/10.1364/OE.531999},
	author = {Ben Huyge and Jens Renders and Joaquim Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@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}
}
@inproceedings {2352,
	title = {Comparison of diffusion kurtosis tensor imaging and multi-tissue CSD for the investigation of group differences in Alzheimer{\textquoteright}s disease},
	booktitle = {International Society of Magnetic Resonance in Medicine (ISMRM)},
	year = {2023},
	address = {Toronto, ON, Canada},
	url = {https://submissions.mirasmart.com/ISMRM2023/Itinerary/PresentationDetail.aspx?evdid=6363},
	author = {Diana Giraldo and Robert E. Smith and Hanne Struyfs and Ellis Niemantsverdriet and Ellen De Roeck and Maria Bjerke and Sebastiaan Engelborghs and Eduardo Romero and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2287,
	title = {A condensed history approach to x-ray dark field effects in edge illumination phase contrast simulations},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	author = {Nicholas Francken and Jonathan Sanctorum and Jens Renders and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@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 {2283,
	title = {DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2023},
	pages = {1-4},
	doi = {10.1109/ISBI53787.2023.10230697},
	author = {Jens Renders and Banafshe Shafieizargar and Marleen Verhoye and Jan De Beenhouwer and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2354,
	title = {Exploring anatomical brain heterogeneity in subjects with mild cognitive impairment},
	booktitle = {2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM)},
	year = {2023},
	pages = {1-5},
	doi = {10.1109/SIPAIM56729.2023.10373453},
	author = {Pabon Ochoa, German A. and Diana Giraldo and Romero, Eduardo}
}
@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 {2304,
	title = {ImWIP: open-source image warping toolbox with adjoints and derivatives},
	journal = {SoftwareX},
	volume = {24},
	year = {2023},
	pages = {101524},
	doi = {https://doi.org/10.1016/j.softx.2023.101524},
	author = {Jens Renders and Ben Jeurissen and Anh-Tuan Nguyen and Jan De Beenhouwer and Jan Sijbers}
}
@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}
}
@article {2270,
	title = {Prolonged microgravity induces reversible and persistent changes on human cerebral connectivity},
	journal = {Communications Biology},
	volume = {6},
	year = {2023},
	doi = {https://doi.org/10.1038/s42003-022-04382-w},
	author = {Steven Jillings and Ekaterina V. Pechenkova and Elena Tomilovskaya and Ilya Rukavishnikov and Ben Jeurissen and Angelique Van Ombergen and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Annen, Jitka and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Petrovichev, Victor and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, P and Steven S L Laureys and Athena Demertzi and Floris L Wuyts}
}
@inproceedings {2279,
	title = {Region-based motion-compensated iterative reconstruction technique for dynamic computed tomography},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2023},
	address = {Cartagena de Indias, Colombia},
	doi = {https://doi.org/10.1109/ISBI53787.2023.10230608},
	author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@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 {2239,
	title = {An accelerated motion-compensated iterative reconstruction technique for dynamic computed tomography},
	booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122421F},
	year = {2022},
	address = {San Diego, CA, United States},
	doi = {https://doi.org/10.1117/12.2631570},
	author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2192,
	title = {Analytic derivatives of scaling motion-compensated projection operators for dynamic computed tomography},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = { 27 (3)},
	year = {2022},
	pages = {1-5},
	doi = {https://doi.org/10.58286/26588},
	url = {https://www.ndt.net/search/docs.php3?id=26588},
	author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer}
}
@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}
}
@article {2216,
	title = {Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight},
	journal = {Front. Neural Circuits},
	volume = {16},
	year = {2022},
	doi = {https://doi.org/10.3389/fncir.2022.815838},
	author = {Andrei Doroshin and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Ekaterina V. Pechenkova and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Ilya Rukavishnikov and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Steven S L Laureys and Petrovichev, Victor and Angelique Van Ombergen and Jitka Annen and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, Peter and Karol Osipowicz and Floris L Wuyts}
}
@mastersthesis {2218,
	title = {Computational anatomy strategies for characterization of brain patterns associated with Alzheimer{\textquoteright}s disease},
	volume = {PhD in Science},
	year = {2022},
	type = {PhD thesis},
	author = {Diana Giraldo}
}
@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}
}
@article {2215,
	title = {The effect of prolonged Spaceflight on Cerebrospinal Fluid and Perivascular Spaces of Astronauts and Cosmonauts},
	journal = {PNAS},
	volume = {119},
	year = {2022},
	chapter = {e2120439119},
	doi = {https://doi.org/10.1073/pnas.2120439119},
	author = {Giuseppe Barisano and Farshid Sepehrband and Heather R. Collins and Steven Jillings and Ben Jeurissen and Andrew J. Taylor and Catho Schoenmaekers and Chloe De Laet and Ilya Rukavishnikov and Inna Nosikova and Alena Rumshiskaya and Jitka Annen and Jan Sijbers and Steven S L Laureys and Angelique Van Ombergen and Petrovichev, Victor and Valentin Sinitsyn and Ekaterina V. Pechenkova and Alexey Grishin and Peter zu Eulenburg and Meng Law and Stefan Sunaert and Paul M. Parizel and Elena Tomilovskaya and Donna Roberts and Floris L Wuyts}
}
@inproceedings {2240,
	title = {Efficient X-ray projection of triangular meshes based on ray tracing and rasterization},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420W },
	doi = {https://doi.org/10.1117/12.2633448},
	author = {Pavel Paramonov and Jens Renders and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2209,
	title = {Evaluation of a Morphable Anthropomorphic Articulated Total Body Model},
	booktitle = {Design Tools and Methods in Industrial Engineering II - Proceedings of the 2nd International Conference on Design Tools and Methods in Industrial Engineering, ADM 2021},
	year = {2022},
	pages = {761{\textendash}772},
	publisher = {Springer},
	organization = {Springer},
	abstract = {In this work a new approach for the creation of Articulated Total Body (ATB) models for person-specific multi-body simulations is presented, with the main aim of overcoming limitations related to classical multi-ellipsoids ATB models, based on regression equations having only the weight and the height of the subject as input. The new methodology is based on a Statistical Shape Model (SSM), morphable according to up to 24 input parameters: the SSM was obtained from Principal Component Analysis (PCA), applied on a wide database of 3D human scans (CAESAR). The so obtained geometry can be segmented automatically to generate body segments with the respective inertial properties (mass, principal moments of inertia, and centres of mass location). The routine has been tested on a random set of 20 male subjects and the classical multi-ellipsoids models were compared to these in terms of inertial properties and 3D external geometry: the highest differences were registered at the abdomen and the thighs for what concerns the mass (60\%), principal moments (75\%) and centres of mass (50 mm) properties; the trunk, the shoulder and the calves are the most critical areas for the external geometry (average distance between the anthropomorphic and ellipsoids models equal to 50 mm). A contribution has been made to build person-specific multibody models. This is a valuable method since approximations made by multi-ellipsoidal models have resulted to be relevant at specific body areas, and personalised models can be a support to design and to forensic analyses.},
	keywords = {3D parametric human model, Articulated total body, Forensic biomechanics, Multibody analysis, principal component analysis (PCA)},
	isbn = {978-3-030-91233-8},
	doi = {10.1007/978-3-030-91234-5_77},
	author = {Giulia Pascoletti and Toon Huysmans and Paolo Conti and Zanetti, Elisabetta M.},
	editor = {Caterina Rizzi and Francesca Campana and Michele Bici and Francesco Gherardini and Tommaso Ingrassia and Paolo Cicconi}
}
@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}
}
@inproceedings {2231,
	title = {Hyperspectral clustering using atrous spatial-spectral convolutional network},
	booktitle = {IGARSS 2022, International Geoscience and Remote Sensing Symposium},
	year = {2022},
	address = {Kuala Lumpur, Malaysia},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@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 {2265,
	title = {Investigating tissue-specific abnormalities in Alzheimer{\textquoteright}s disease with multi-shell diffusion MRI},
	journal = {Journal of Alzheimer{\textquoteright}s Disease},
	volume = {90},
	year = {2022},
	pages = {1771-1791},
	doi = {10.3233/JAD-220551},
	author = {Diana Giraldo and Robert Elton Smith and Hanna Struyfs and Ellis Niemantsverdriet and Ellen De Roeck and Maria Bjerke and Sebastiaan Engelborghs and Romero, Eduardo and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2242,
	title = {Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420O},
	doi = {https://doi.org/10.1117/12.2633587},
	author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@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 {2260,
	title = {MS2A-Net: multi-view spectral-spatial association network for hyperspectral image clustering},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {15},
	year = {2022},
	pages = {6518-6530},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Richard Gloaguen 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}
}
@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 {2207,
	title = {Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing },
	volume = {15},
	year = {2022},
	month = {1/2022},
	pages = {284-296},
	abstract = {The ever-growing developments in technology to capture different types of image data (e.g., hyperspectral imaging and Light Detection and Ranging (LiDAR)-derived digital surface model (DSM)), along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this paper, we propose a multi-sensor deep clustering (MDC) algorithm for the joint processing of multi-source imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multi-sensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at: https://github.com/Kasra2020/MDC.},
	doi = {10.1109/JSTARS.2021.3132856},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2144,
	title = {Adjoint image warping using multivariate splines with application to 4D-CT},
	journal = {Medical Physics},
	volume = {48},
	year = {2021},
	pages = {6362-6374},
	doi = {10.1002/mp.14765},
	author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@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 {2135,
	title = {Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil{\textendash}water three phase flows},
	journal = {Measurement},
	volume = {168},
	year = {2021},
	chapter = {108427},
	abstract = {In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil{\textendash}water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.},
	doi = {10.1016/j.measurement.2020.108427},
	author = {Mohammadmehdi Roshani and Giang Phan and Gholam Hossein Roshani and Robert Hanus and Enrico Corniani and Ehsan Nazemi}
}
@article {2049,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Following Primary Repair with Internal Bracing: a Longitudinal Study},
	journal = {Journal of Orthopaedic Research },
	volume = {39},
	year = {2021},
	pages = {1318{\textendash}1330},
	doi = {10.1002/jor.24684},
	author = {Pieter Van Dyck and Martijn Froeling and Christiaan H.W. Heusdens and Jan Sijbers and Annemie Ribbens and Thibo Billiet}
}
@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 {2092,
	title = {FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom},
	journal = {Optics Express},
	volume = {29},
	year = {2021},
	pages = {3438-3457},
	doi = {https://doi.org/10.1364/OE.409982},
	author = {Bjorn De Samber and Jens Renders and Tim Elberfeld and Yves Maris and Jonathan Sanctorum and Nathana{\"e}l Six and Liang, Zhihua and Jan De Beenhouwer and Jan Sijbers}
}
@article {2203,
	title = {Gauss-Newton-Krylov for Reconstruction of Polychromatic X-ray CT Images},
	journal = {IEEE Transactions on Computational Imaging},
	volume = {7},
	year = {2021},
	pages = {1304-1313},
	doi = {10.1109/TCI.2021.3133226},
	author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2179,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {NeuroImage},
	volume = {240},
	year = {2021},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118367},
	author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fernandez and Evren Ozarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fenxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas and Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G Schilling}
}
@inproceedings {2198,
	title = {Mesh-based reconstruction of dynamic foam images using X-ray CT},
	booktitle = {International Conference on 3D Vision (3DV2021)},
	year = {2021},
	pages = {1312-1320},
	doi = {10.1109/3DV53792.2021.00138},
	author = {Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@conference {2178,
	title = {Motion compensating X-ray micro-CT of diamonds in a processing stage},
	year = {2021},
	month = {September},
	author = {Jens Renders and Anh-Tuan Nguyen and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2222,
	title = {A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2021},
	doi = {10.1109/WHISPERS52202.2021.9483953},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and R. Jackisch and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2164,
	title = {Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning},
	journal = {Expert Systems with Applications},
	volume = {176},
	year = {2021},
	pages = {1-12},
	doi = {https://doi.org/10.1016/j.eswa.2021.114925},
	author = {Tim Van De Looverbosch and Ellen Raeymaekers and Pieter Verboven and Jan Sijbers and Bart Nicolai}
}
@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 {2196,
	title = {Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer{\textquoteright}s disease dementia},
	journal = {Alzheimer{\textquoteright}s \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
	volume = {13},
	year = {2021},
	pages = {e12237},
	doi = {https://doi.org/10.1002/dad2.12237},
	author = {Diana Giraldo and Jan Sijbers and Romero, Eduardo}
}
@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}
}
@inproceedings {2224,
	title = {When is the right time to apply denoising?},
	booktitle = {IGARSS 2021, International Geoscience and Remote Sensing Symposium},
	year = {2021},
	address = {Brussels, Belgium},
	doi = {10.1109/IGARSS47720.2021.9553263},
	author = {Kasra Rafiezadeh Sahi and Behnood Rasti and Pedram Ghamisi and Paul Scheunders and Richard Gloaguen}
}
@inproceedings {2028,
	title = {Adjoint pairs of image warping operators for motion modeling in 4D-CT},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@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 {2136,
	title = {Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter},
	journal = {Flow Measurement and Instrumentation},
	volume = {75},
	year = {2020},
	abstract = {Multiphase flowmeters have an important role to play in the industry and any attempts that lead to improvements in this field are of great interest. In the current study, group method of data handling (GMDH) technique was applied in order to increase measuring precision of a simple photon attenuation based two-phase flowmeter that has the ability to estimate the gas volumetric percentage in a two-phase flow without any dependency to flow regime pattern. The simple photon attenuation based system is comprised of a cobalt-60 radioisotope and only one 25.4 mm {\texttimes} 25.4 mm sodium iodide crystal detector. Four extracted features from recorded photon spectrum in sodium iodide crystal detector were used as the inputs of GMDH neural network. Equations related to the combination of the features and the error rate of each approximation is also reported in this paper. Applying the mentioned technique, the gas volumetric percentage in an oil-gas two phase flow was determined with the root mean square error of less than 2.71 without any dependency to the flow pattern. The obtained measuring precision in this study is at least 2.1 times better than reported in previous studies.},
	doi = {10.1016/j.flowmeasinst.2020.101804},
	author = {Mohammadmehdi Roshani and Mohammad Amir Sattari and Peshawa Jammal Muhammad Ali and Gholam Hossein Roshani and Enrico Corniani and Ehsan Nazemi}
}
@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}
}
@inproceedings {1951,
	title = {A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling},
	booktitle = {Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping},
	volume = {975},
	year = {2020},
	pages = {464{\textendash}470},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In times of online shopping, it is a challenge to select the right size of the desired clothing without fitting it before ordering. Therefore, this study describes three techniques to predict 3D upper body dimensions. The first method used basic personal parameters (gender, age, weight and length), the second technique used also the shoulder width and the last method used a 3D Styku scan to add extra input parameters. The accuracy of the three prediction methods was compared against hand measurements for 17 upper body dimensions of 37 subjects. The Intraclass Correlation Coefficient increases with 11.2\% for the Styku method compared to the other methods. For chest, hip and waist measurements, the basic method is reliable to predict 3D body dimensions and indicate the right size from an existing collection. For more accurate upper body dimensions as needed for producing custom made clothing, a 3D Styku scan can supply the desired input.},
	isbn = {978-3-030-20216-3},
	doi = {10.1007/978-3-030-20216-3_43},
	author = {Peeters, Thomas and Vleugels, Jochen and Stijn Verwulgen and Femke Danckaers and Toon Huysmans and Jan Sijbers and De Bruyne, Guido},
	editor = {Di Nicolantonio, Massimo and Rossi, Emilio and Alexander, Thomas}
}
@article {2093,
	title = {Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm},
	journal = {Remote Sensing},
	volume = {12 (23)},
	year = {2020},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and R. Jackisch and Paul Scheunders and Richard Gloaguen}
}
@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 {2064,
	title = {Diffusion tensor imaging of the anterior cruciate ligament graft following reconstruction: a longitudinal study},
	journal = {European Radiology},
	volume = {34},
	year = {2020},
	pages = {6673{\textendash}6684},
	doi = {10.1007/s00330-020-07051-w},
	author = {Pieter Van Dyck and Thibo Billiet and Damien Desbuquoit and Peter Verdonk and Christiaan H. Heusdens and Ella Roelant and Jan Sijbers and Martijn Froeling}
}
@article {2037,
	title = {Harmonisation of Brain Diffusion MRI: Concepts and Methods},
	journal = {Frontiers in Neuroscience },
	volume = {14},
	year = {2020},
	month = {03/2020},
	pages = {1-17},
	doi = {https://doi.org/10.3389/fnins.2020.00396},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker 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}
}
@conference {2021,
	title = {Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers},
	year = {2020},
	url = {https://www.ismrm-benelux.org/wp-content/uploads/2020/01/Proceedings2020.pdf},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1971,
	title = {Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study},
	journal = {Fundamenta Informaticae},
	volume = {172},
	year = {2020},
	pages = {105{\textendash}128},
	abstract = {Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson{\textquoteright}s disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.},
	doi = {10.3233/FI-2020-1896},
	author = {Quinten Beirinckx and Gabriel Ramos-Llord{\'e}n and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Marleen Verhoye and Jan Sijbers and Arnold Jan den Dekker}
}
@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}
}
@inproceedings {2133,
	title = {Newton-Krylov Methods For Polychromatic X-Ray CT},
	booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},
	year = {2020},
	pages = {3045-3049},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Abu Dhabi},
	keywords = {Attenuation, computed tomography, image reconstruction, Jacobian matrices, Linear programming, Mathematical model, Structural beams},
	isbn = {978-1-7281-6396-3},
	doi = {10.1109/ICIP40778.2020.9190717},
	url = {https://ieeexplore.ieee.org/abstract/document/9190717},
	author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2134,
	title = {Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products},
	journal = {Nuclear Engineering and Technology},
	year = {2020},
	type = {Research regular paper},
	abstract = {It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm {\texttimes} 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.},
	author = {Mohammadmehdi Roshani and Giang Phan and Rezhna Hassan Faraj and Nhut-Huan Phan and Gholam Hossein Roshani and Enrico Corniani and Ehsan Nazemi}
}
@article {2031,
	title = {Super-Resolution Magnetic Resonance Imaging of the Knee Using 2-Dimensional Turbo Spin Echo Imaging},
	journal = {Investigative Radiology},
	volume = {55},
	year = {2020},
	pages = {481-493},
	doi = {10.1097/RLI.0000000000000676},
	author = {Pieter Van Dyck and Celine Smekens and Floris Vanhevel and Eline De Smet and Ella Roelant and Jan Sijbers and Ben Jeurissen}
}
@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 {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 {1965,
	title = {Deep learning based missing wedge artefact removal for electron tomography},
	year = {2019},
	pages = {660-661},
	author = {Juho Rimpelainen and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer}
}
@article {1963,
	title = {MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.},
	journal = {Neuroimage},
	year = {2019},
	month = {2019 Aug 29},
	pages = {116137},
	abstract = {<p>MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.</p>},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2019.116137},
	author = {Tournier, J-Donald and Smith, Robert and Raffelt, David and Tabbara, Rami and Dhollander, Thijs and Pietsch, Maximilian and Christiaens, Daan and Ben Jeurissen and Yeh, Chun-Hung and Connelly, Alan}
}
@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}
}
@conference {Wuyts2019-xr,
	title = {Novel insight on effect and recovery of long-duration spaceflight on the ventricles of the space traveller{\textquoteright}s brain},
	volume = {2019},
	year = {2019},
	pages = {IAC{\textendash}19_A1_2_4_x51230},
	publisher = {International Astronautical Federation},
	author = {Floris L Wuyts and Steven Jillings and Angelique Van Ombergen and Ben Jeurissen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and others}
}
@article {1909,
	title = {Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks},
	journal = {Medical Image Analysis},
	volume = {52},
	year = {2019},
	pages = {56-67},
	doi = {https://doi.org/10.1016/j.media.2018.10.009},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Jan Sijbers and Alexander Leemans}
}
@conference {2022,
	title = {Voxelwise harmonisation of FA on a cohort of 605 healthy subjects using ComBat: an exploratory study},
	year = {2019},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1827,
	title = {Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	pages = {5664},
	address = {Paris, France},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2006,
	title = {Automatic Detection of Surface Damages on Steel Structures using Near Infrared Hyperspectral Imaging},
	year = {2018},
	abstract = {Surface damages such as corrosion and coating delamination are very common on steel structures and, if not prevented, can accelerate degradation and eventually lead to failure of the whole structure. Hence, field monitoring is of utmost importance to guarantee the structural health and reduce the maintenance cost. Visual inspection is very perilous (especially for offshore structures), time-consuming, and largely depends on the experience of the inspector (Price \& Figueira 2017). Current non-destructive testing (NDT) techniques (e.g. based on ultrasonic or eddy current technology, acoustic emission ([Calabrese et al 2016; Wu et al 2016] or Electrical Resistance [ Li et al 2007; Mathiesen et al 2016]), require a direct contact with the surface, which causes difficulties when the structure or parts of it are not accessible.
Image processing techniques have been considered as a fast and easier alternative to contact-based techniques. These techniques are usually based on measuring the textural and morphological changes in digital images from the structures (Feliciano et al 2015; Li \& Cheng 2016). However, in reality other materials on steel structures such as dirt, paint, bird droppings, oil leakage, and biofouling can significantly complicate the inspection. Moreover, some effects of corrosion not necessarily reveal themselves in the visible wavelength region. Hyperspectral imaging outside the visible range is an advanced imaging technique that can reveal chemical-mineralogical surface changes that are not observable by the naked eye.
This paper studied the feasibility of detecting coating damages on steel components using hyperspectral imaging. Two different samples of industrial components, provided by ENGIE Laborelec, were scanned with a hyperspectral camera: a part of a flame tube of a heavy-duty gas turbine as well as an artificially corroded sample of coated steel. The images were collected with a snapshot hyperspectral camera in the visible and Near Infrared range (manufactured by IMEC) and analyzed using a k-means clustering method. The outputs showed that despite the visual similarities between coating layers, corroded, and base material, hyperspectral imaging was capable to distinguish between them. Spectral plots showed that differences in spectral behavior of corroded and coated materials were more apparent when the wavelength shifts from the visible towards the near-infrared range. While further investigation is needed, the current results demonstrated that hyperspectral image analysis is a promising technology to complement or even replace digital image analysis within the visible range for characterizing steel surfaces in situ.},
	author = {Zohreh Zahiri and Bart Ribbens and Steve Vanlanduit and Paul Scheunders}
}
@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}
}
@article {1731,
	title = {Discrete tomography in an in vivo small animal bone study},
	journal = {Journal of Bone and Mineral Metabolism},
	volume = {36},
	year = {2018},
	pages = {40{\textendash}53},
	doi = {10.1007/s00774-017-0815-x},
	author = {Elke Van de Casteele and Egon Perilli and Wim Van Aarle and Karen Reynolds and Jan Sijbers}
}
@conference {1828,
	title = {An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {MRI relaxometry holds the promise of providing biomarkers for monitoring, staging and follow up of diseases. Imperative to meet minimum standards for objective, reproducible, and reliable biomarkers is the need for accurate, precise, quantitative parameters maps, such as T1~or T2. While unrealistic physical modelling is often argued as the main cause of lack of accuracy, little effort has been made on discussing the impact that inadequate parameter estimation methods have on the accuracy and precision of MRI relaxometry techniques. This educational poster attempts to introduce young MR students/researchers into the basics of modern statistical parameter estimation theory, and its application for accurate and precise relaxometry. 
},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@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}
}
@mastersthesis {1832,
	title = {Improved MRI Relaxometry through Statistical Signal Processing},
	volume = { Doctor of Science},
	year = {2018},
	month = {02/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {Magnetic Resonance Imaging (MRI) relaxometry is a quantitative MRI modality that deals with the estimation of the spin-lattice, T1, and the spin-spin, T2, relaxation times. Both relaxation times are fundamental parameters that describe the spin dynamics within a tissue during the relaxation process of the Nuclear Magnetic Resonance (NMR) phenomenon. During the last decades, spatial T1 and T2 maps have been analyzed to study and monitor the states of a multitude of human diseases. Those studies have shown that MRI relaxometry holds the promise of generating robust, objective image-based biomarkers for central nervous system pathologies, cardiovascular diseases and beyond. Unfortunately, quantitative biomarkers derived from MRI relaxometry are not yet sufficiently specific, sensitive, and robust to be routinely used in clinical practice. On top of that, high-resolution relaxation maps demand a clinically unfeasible long scanning time.

This PhD thesis tries to reduce the obstacles that preclude MRI relaxometry from being a fast, accurate, and precise quantitative MRI modality for clinical use by improving the way MR relaxometry data are acquired, processed and analyzed. In particular, by adopting a statistical signal processing approach, three contributions that address common problems of the field are given. Firstly, we present a unified relaxometry-based processing framework where the T1 map estimation and motion correction are accounted for in a synergistic manner, being both the T1 map and the motion parameters simultaneously estimated with a Maximum-Likelihood estimator. It is demonstrated that substantially more accurate T1 maps are obtained with our proposed integrated approach in comparison to the yet typical but suboptimal two-step approach: T1 model fitting after image registration.  Secondly, we developed a fast, robust, T1 estimator for Variable Flip Angle (VFA) T1 mapping that can provide statistically optimal T1 estimates with unprecedentedly short computation time, thereby enabling optimal, real-time, VFA T1 mapping.  Finally, we were able to reduce the long overall scanning time of MRI relaxometry studies using our novel k-space reconstruction technique that permits the reconstruction of individual MR images with less number of samples than it is commonly required.},
	author = {Gabriel Ramos-Llord{\'e}n}
}
@article {1843,
	title = {Modeling brain dynamics in brain tumor patients using The Virtual Brain},
	journal = {eNeuro},
	year = {2018},
	abstract = {Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 brain tumor patients and 11 control subjects using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.},
	doi = {10.1101/265637},
	url = {https://www.biorxiv.org/content/early/2018/05/07/265637},
	author = {Aerts, Hannelore and Schirner, Michael and Ben Jeurissen and Van Roost, Dirk and Eric Achten and Ritter, Petra and Marinazzo, Daniele}
}
@article {1902,
	title = {Noise reduction in hyperspectral imagery: overview and application},
	journal = {Remote Sensing },
	volume = {10},
	year = {2018},
	pages = {482},
	author = {B Rasti and Paul Scheunders and P Ghesami and G Licciardi and Jocelyn Chanussot}
}
@article {1842,
	title = {NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2018},
	pages = {2414 - 2427},
	doi = {10.1109/TMI.2018.2833288},
	author = {Gabriel Ramos-Llord{\'e}n and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcus Bj{\"o}rk and Floris Vanhevel and Paul M Parizel and Raul San Jos{\'e} Est{\'e}par and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {Jeurissen2018-zz,
	title = {Super-resolution for spherical deconvolution of multi-shell diffusion MRI data},
	year = {2018},
	pages = {36},
	author = {Ben Jeurissen and Ramos-Llord{\'e}n, Gabriel and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@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 {1792,
	title = {TomoBank: A Tomographic Data Repository for Computational X-ray Science},
	journal = {Measurement Science and Technology},
	volume = {29},
	year = {2018},
	pages = {1-10},
	doi = {https://doi.org/10.1088/1361-6501/aa9c19},
	author = {Francesco De Carlo and Doga Gursoy and Daniel Ching and Kees Joost Batenburg and Ludwig, Wolfgang and Lucia Mancini and Federica Marone Welford and Rajmund Mokso and Daan Pelt and Jan Sijbers and Mark Rivers}
}
@article {1752,
	title = {Altered functional brain connectivity in patients with visually induced dizziness},
	journal = {NeuroImage: Clinical},
	volume = {14},
	year = {2017},
	pages = {538{\textendash}545},
	doi = {http://dx.doi.org/10.1016/j.nicl.2017.02.020},
	author = {Angelique Van Ombergen and Lizette Heine and Steven Jillings and Edward Roberts and Ben Jeurissen and V. Van Rompaey and Viviana Mucci and Stefanie Vanhecke and Jan Sijbers and Floris Vanhevel and Stefan Sunaert and Mohamed Ali Bahri and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@article {1806,
	title = {Evaluating palmiet wetland decline: a comparison of three methods},
	journal = {Remote Sensing Applications: Society and Environment},
	volume = {8},
	year = {2017},
	pages = {212-223},
	author = {A J Rebelo and Paul Scheunders and K J Esler and P Meire}
}
@conference {Giraldo2017-nq,
	title = {Fixel-Based Analysis of Alzheimer{\textquoteright}s Disease Using Multi-Tissue Constrained Spherical Deconvolution of Multi-Shell Diffusion MRI},
	year = {2017},
	pages = {400},
	author = {Diana Giraldo and Struyfs, Hanne and Raffelt, David and Paul M Parizel and Sebastiaan Engelborghs and Romero, Eduardo and Jan Sijbers and Ben Jeurissen}
}
@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}
}
@mastersthesis {1777,
	title = {Improved reliability of fiber orientation estimation and graph theoretical analysis of structural brain networks with diffusion MRI},
	volume = {Doctor of Science},
	year = {2017},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Timo Roine}
}
@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 {1722,
	title = {Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {1041 - 1053},
	doi = {10.1109/TMI.2016.2645122},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1818,
	title = {Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation},
	year = {2017},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Piet Bladt and A. Cuyt and Jan Sijbers}
}
@article {1706,
	title = {A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {433 - 446},
	doi = {10.1109/TMI.2016.2611653},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Ben Jeurissen and Floris Vanhevel and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1641,
	title = {Automatic forensic analysis of automotive paints using optical microscopy},
	journal = {Forensic Science International},
	volume = {259},
	year = {2016},
	pages = {210-220},
	author = {Guy Thoonen and B. Nys and Y. Vander Haegen and G. De Roy and Paul Scheunders}
}
@conference {1694,
	title = {Building a statistical shape model of the interior and exterior of the bell pepper},
	volume = {28-30 November},
	year = {2016},
	address = {Vienna, Austria},
	author = {Femke Danckaers and Toon Huysmans and Seppe Rogge and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@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}
}
@article {1651,
	title = {D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data},
	journal = {PlosOne},
	volume = {11},
	number = {3},
	year = {2016},
	pages = {1-23},
	doi = {10.1371/journal.pone.0149778},
	author = {Daniele Perrone and Ben Jeurissen and Jan Aelterman and Timo Roine and Jan Sijbers and Aleksandra Pizurica and Alexander Leemans and Wilfried Philips}
}
@article {1620,
	title = {Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone-induced demyelination and spontaneous remyelination},
	journal = {NeuroImage},
	volume = {125},
	year = {2016},
	pages = {363{\textendash}377},
	doi = {10.1016/j.neuroimage.2015.10.052},
	author = {Caroline Guglielmetti and Jelle Veraart and Ella Roelant and Zhenhua Mai and Jasmijn Daans and Johan Van Audekerke and Maarten Naeyaert and Greetje Vanhoutte and Rafael  Delgado Y Palacios and Jelle Praet and Els Fieremans and Peter Ponsaerts and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@conference {Roine2016-je,
	title = {Methodological Considerations on Graph Theoretical Analysis of Structural Brain Networks},
	year = {2016},
	pages = {3437},
	author = {Roine, Timo and Ben Jeurissen and Perrone, Daniele and Jan Aelterman and Philips, Wilfried and Jan Sijbers and Leemans, Alexander}
}
@inproceedings {1653,
	title = {Multi-voxel algorithm for quantitative bi-exponential MRI T1 estimation},
	booktitle = {SPIE Medical Imaging},
	volume = {9784},
	year = {2016},
	pages = {978402},
	address = {San Diego, California, United States of America},
	abstract = {In this work, we propose a joint multi-voxel bi-exponential estimator (JMBE) for quantitative bi-exponential T1 estimation in magnetic resonance imaging, to account for partial volume effects and to yield more accurate results compared to single-voxel bi-exponential estimators (SBEs). Using a numerical brain phantom with voxels containing two tissues, the minimal signal-to-noise ratio (SNR) needed to estimate both T1 values with sufficient accuracy was investigated. Compared to the SBE, and for clinically achievable single-voxel SNRs, the JMBE yields accurate T1 estimates if four or more neighboring voxels are used in the joint estimation framework, in which case it is also efficient.},
	doi = {http://dx.doi.org/10.1117/12.2216831},
	author = {Piet Bladt and Gwendolyn Van Steenkiste and Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1647,
	title = {NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals},
	year = {2016},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Marcus Bj{\"o}rk and Marleen Verhoye and Jan Sijbers}
}
@article {1690,
	title = {T1 relaxometry of crossing fibres in the human brain.},
	journal = {NeuroImage},
	year = {2016},
	month = {07/2016},
	abstract = {A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2016.07.037},
	author = {Silvia De Santis and Yaniv Assaf and Ben Jeurissen and Derek K. Jones and Roebroeck, Alard}
}
@conference {1765,
	title = {Understanding microstructural deformation of apple tissue from 4D micro-CT imaging},
	year = {2016},
	doi = {10.17660/ActaHortic.2018.1197.2},
	author = {Wang, Z and Seppe Rogge and Mattias Van Dael and Vincent Van Nieuwenhove and Pieter Verboven and Jan Sijbers and Bart Nicolai}
}
@article {1634,
	title = {Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder},
	journal = {Molecular Autism},
	volume = {6},
	number = {1},
	year = {2015},
	pages = {65},
	abstract = {Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain{\textquoteright}s wiring diagram, i.e., the connectome.
We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60{\textendash}90 \% of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.
In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.
Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.},
	keywords = {Autism spectrum disorder, Brain networks, Connectivity, Connectome, Diffusion Magnetic Resonance Imaging, Graph theoretical analysis, Tractography, White matter tract},
	issn = {2040-2392},
	doi = {10.1186/s13229-015-0058-4},
	url = {http://dx.doi.org/10.1186/s13229-015-0058-4},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Tani, Pekka and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Caeyenberghs, Karen and Alexander Leemans and Sams, Mikko}
}
@article {1518,
	title = {Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome},
	journal = {Molecular Autism},
	volume = {6},
	year = {2015},
	pages = {4},
	abstract = {Background: The aim of this study was to investigate potential differences in neural structure in individuals with Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms in AS are severe impairments in social interaction, and restricted or repetitive patterns of behavior, interests or activities. Methods: Diffusion weighted magnetic resonance imaging data was acquired for 14 adult males with AS and 19 age, gender and IQ-matched controls. Voxel-wise group differences in fractional anisotropy (FA) were studied with Tract-Based Spatial
Statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (e.g. crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and the severity of symptoms, we looked for correlations between FA and Autism Spectrum Quotient (AQ), Empathy Quotient and Systemizing Quotient. Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in the temporal part of superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts. However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not explained by the complexity of microstructural organization, measured using the planar diffusion coefficient. In addition, we found a correlation between AQ and FA in the right IFO in the whole group. Conclusions: Our results suggest that there are local and tract-level abnormalities in WM microstructure in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.},
	doi = {10.1186/2040-2392-6-4},
	url = {http://www.molecularautism.com/content/6/1/4},
	author = {Roine, Ulrika and Salmi, Juha and Timo Roine and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Alexander Leemans and Sams, Mikko}
}
@article {1604,
	title = {Diffusion Kurtosis Imaging: a possible MRI biomarker for AD diagnosis?},
	journal = {Journal of Alzheimer{\textquoteright}s Disease},
	volume = {48},
	number = {4},
	year = {2015},
	pages = {937-948},
	doi = {10.3233/JAD-150253},
	author = {Hanna Struyfs and Wim Van Hecke and Jelle Veraart and Jan Sijbers and Sylvie Slaets and Maya De Belder and Laura Wuyts and Benjamin Peters and Kristel Sleegers and Caroline Robberecht and Van Broeckhoven, Christine and Frank De Belder and Paul M Parizel and Sebastiaan Engelborghs}
}
@conference {1594,
	title = {Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone-induced demyelination and spontaneous remyelination},
	volume = {23},
	number = {4332},
	year = {2015},
	author = {Caroline Guglielmetti and Jelle Veraart and Ella Roelant and Zhenhua Mai and Jasmijn Daans and Johan Van Audekerke and Jelle Praet and Peter Ponsaerts and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@article {1519,
	title = {Informed constrained spherical deconvolution (iCSD)},
	journal = {Medical Image Analysis},
	volume = {24},
	year = {2015},
	pages = {269{\textendash}281},
	type = {Original research},
	chapter = {269},
	abstract = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels.
In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data.
Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.},
	keywords = {Connectomics, constrained spherical deconvolution, Diffusion MRI, fiber orientation, gray matter, partial volume effect, Response function, Tractography},
	doi = {10.1016/j.media.2015.01.001},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841515000080},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@conference {1661,
	title = {Inversion Recovery DTI In Vivo at 7T in the Human Brain},
	year = {2015},
	pages = {567},
	address = {Toronto, Ontario, Canada},
	author = {Silvia De Santis and Ben Jeurissen and Derek K. Jones and Yaniv Assaf and Alard Roebroek}
}
@article {roine2015isotropic,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	journal = {Information-based methods for neuroimaging: analyzing structure, function and dynamics},
	year = {2015},
	pages = {112},
	publisher = {Frontiers Media SA},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Alexander Leemans and Wilfried Philips 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}
}
@article {1529,
	title = {Nonlinear unmixing by using different metrics in a linear unmixing chain},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders and Paul Gader and A. Rangarajan}
}
@article {1587,
	title = {Online Tomato Inspection Using X-Ray Radiographies and 3- Dimensional Shape Models},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {37-42},
	isbn = {978-88-95608-35-8},
	doi = {10.3303/CET1544007},
	author = {Mattias Van Dael and Seppe Rogge and Pieter Verboven and Wouter Saeys and Jan Sijbers and Bart Nicolai}
}
@conference {1561,
	title = {Partial discreteness: a new type of prior knowledge for MRI reconstruction},
	volume = {23},
	year = {2015},
	pages = {3417},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {7351081,
	title = {Partially discrete magnetic resonance tomography},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {1653-1657},
	keywords = {Bayes methods, Bayesian segmentation, Bayesian segmentation regularization, biomedical MRI, Breast, breast implant MR images, computerised tomography, Discrete tomography, image reconstruction, image representation, image segmentation, Implants, medical image processing, MR angiography images, MR image reconstruction, MRI, partially discrete magnetic resonance tomography, reconstruction, Tomography, TV},
	doi = {10.1109/ICIP.2015.7351081},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	year = {2015},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	volume = {23},
	year = {2015},
	pages = {447},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {7351386,
	title = {Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {3160-3164},
	keywords = {alignment, Approximation methods, biomedical MRI, image restoration, interpolation effect, Magnetic Resonance Imaging, maximum likelihood approach, maximum likelihood estimation, medical image processing, ML approach, ML restoration approach, motion estimation, motion model parameters, quantitative T1 mapping, registration, relaxometry, Rician channels, Signal to noise ratio, simultaneous motion correction, Standards, T1 estimation, T1 mapping, T1-weighted images, tissue spin-lattice relaxation time, voxel-wise estimation},
	doi = {10.1109/ICIP.2015.7351386},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1535,
	title = {Subcortical volumetric changes across the adult lifespan: subregional thalamic atrophy accounts for age-related sensorimotor performance declines},
	journal = {Cortex},
	volume = {65},
	year = {2015},
	pages = {128-138},
	doi = { 10.1016/j.cortex.2015.01.003},
	author = {L. Serbruyns and Inge Leunissen and Toon Huysmans and K. Cuypers and R. L. Meesen and P. van Ruitenbeek and Jan Sijbers and S. P. Swinnen}
}
@conference {roine2014ismrm,
	title = {Improving fiber orientation estimation in constrained spherical deconvolution under non-white matter partial volume effects},
	year = {2014},
	month = {05/2014},
	pages = {4492},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@article {roine2014frontiers,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	journal = {Frontiers in Neuroinformatics},
	volume = {8},
	number = {28},
	year = {2014},
	month = {03/2014},
	pages = {1-9},
	publisher = {Frontiers},
	type = {Original Research},
	abstract = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVE) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple nonparallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. 

In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNR), fiber configurations, and tissue fractions. 

Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50 \% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50 \% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25 \% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. 

The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.
},
	keywords = {constrained spherical deconvolution, Diffusion MRI, fiber orientation, gray matter, partial volume effect},
	doi = {10.3389/fninf.2014.00028},
	url = {http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00028/abstract},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@conference {1443,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	year = {2014},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@article {1481,
	title = {Iterative bilateral filter for Rician noise reduction in MR images},
	journal = {Signal, Image and Video Processing },
	year = {2014},
	doi = {10.1007/s11760-013-0611-6},
	author = {R. Riji and Jeny Rajan and Jan Sijbers and Madhu S. Nair}
}
@booklet {1538,
	title = {Joint motion correction and estimation for T1 mapping: proof of concept},
	howpublished = {Medical Imaging Summer School 2014, Favignana, Italy},
	year = {2014},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1473,
	title = {A memory efficient method for fully three-dimensional object reconstruction with HAADF STEM Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {141},
	year = {2014},
	pages = {22{\textendash}31},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2014.03.008},
	author = {Wouter Van den Broek and A. Rosenauer and Jan Sijbers and Dirk Van Dyck and Sandra Van Aert}
}
@article {1441,
	title = {A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test},
	journal = {Signal Processing},
	volume = {103},
	year = {2014},
	pages = {16-23},
	doi = {http://dx.doi.org/10.1016/j.sigpro.2013.12.018},
	author = {Jeny Rajan and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1530,
	title = {Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Rob Heylen and Paul Scheunders and A. Rangarajan and Paul Gader}
}
@article {1497,
	title = {Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images},
	journal = {Applied Physics Letters},
	volume = {105},
	year = {2014},
	doi = {10.1063/1.4892884},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and G T Martinez and A. Rosenauer and Jan Sijbers and Sandra Van Aert}
}
@article {1480,
	title = {The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography},
	journal = {Computer Vision and Image Understanding},
	volume = {126},
	year = {2014},
	pages = {28-37},
	doi = {10.1016/j.cviu.2014.05.007},
	author = {Roelandts, Tom and Kees Joost Batenburg and Arnold Jan den Dekker and Jan Sijbers}
}
@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}
}
@article {1546,
	title = {Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism.},
	journal = {NeuroImage},
	volume = {90},
	year = {2014},
	month = {2014 Apr 15},
	pages = {280-9},
	abstract = {BACKGROUND: The brain-derived neurotrophic factor (BDNF) val66met polymorphism is associated with altered activity dependent secretion of BDNF and a variable influence on brain morphology and cognition. Although a met-dose effect is generally assumed, to date the paucity of met-homozygotes have limited our understanding of the role of the met-allele on brain structure.

METHODS: To investigate this phenomenon, we recruited sixty normal healthy subjects, twenty in each genotypic group (val/val, val/met and met/met). Global and local morphology were assessed using voxel based morphometry and surface reconstruction methods. White matter organisation was also investigated using tract-based spatial statistics and constrained spherical deconvolution tractography.

RESULTS: Morphological analysis revealed an "inverted-U" shaped profile of cortical changes, with val/met heterozygotes most different relative to the two homozygous groups. These results were evident at a global and local level as well as in tractography analysis of white matter fibre bundles.

CONCLUSION: In contrast to our expectations, we found no evidence of a linear met-dose effect on brain structure, rather our results support the view that the heterozygotic BDNF val66met genotype is associated with cortical morphology that is more distinct from the BDNF val66met homozygotes. These results may prove significant in furthering our understanding of the role of the BDNF met-allele in disorders such as Alzheimer{\textquoteright}s disease and depression.},
	keywords = {Adolescent, Adult, Alleles, Brain, Brain-Derived Neurotrophic Factor, diffusion tensor imaging, Female, Genotype, Heterozygote, Homozygote, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Polymorphism, Single Nucleotide, Young Adult},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.12.050},
	author = {Forde, Natalie J and Ronan, Lisa and Suckling, John and Scanlon, Cathy and Neary, Simon and Holleran, Laurena and Alexander Leemans and Tait, Roger and Rua, Catarina and Fletcher, Paul C and Ben Jeurissen and Dodds, Chris M and Miller, Sam R and Bullmore, Edward T and Colm McDonald and Nathan, Pradeep J and Dara M. Cannon}
}
@article {1343,
	title = {Advanced reconstruction algorithms for electron tomography: from comparison to combination},
	journal = {Ultramicroscopy},
	volume = {127},
	year = {2013},
	pages = {40{\textendash}47},
	author = {Bart Goris and Roelandts, Tom and Kees Joost Batenburg and Hamed Heidari Mezerji and Sara Bals}
}
@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}
}
@conference {1433,
	title = {Analysis and modeling of isotropic partial volume effects in diffusion MRI},
	volume = {7},
	year = {2013},
	month = {2013},
	doi = {10.3389/conf.fninf.2013.10.00027},
	author = {Timo Roine and Ben Jeurissen and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@article {1369,
	title = {Comprehensive framework for accurate diffusion MRI parameter estimation},
	journal = {Magnetic Resonance in Medicine},
	volume = {81},
	year = {2013},
	pages = {972-984},
	doi = {10.1002/mrm.24529},
	author = {Jelle Veraart and Jeny Rajan and Ron R Peeters and Alexander Leemans and Stefan Sunaert and Jan Sijbers}
}
@article {1412,
	title = {Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: A diffusion tensor imaging study},
	journal = {Autism Research},
	volume = {6},
	year = {2013},
	pages = {642-650},
	chapter = {642},
	doi = {10.1002/aur.1332},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Alexander Leemans and Sams, Mikko}
}
@inproceedings {1401,
	title = {Inline 3D X-ray Inspection of Food using Discrete Tomography},
	booktitle = {InsideFood Symposium},
	year = {2013},
	address = {Leuven, Belgium},
	author = {Luis Filipe Alves Pereira and Roelandts, Tom and Jan Sijbers}
}
@mastersthesis {1428,
	title = {Local Prior Knowledge in Tomography},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {11/2013},
	type = {PhD thesis},
	author = {Roelandts, Tom}
}
@inproceedings {1400,
	title = {Localizing DART using the Reconstructed Residual Error},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	volume = {Book of Abstracts: Talks},
	year = {2013},
	pages = {113-116},
	address = {Ghent, Belgium},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1475,
	title = {Misalignment correction for T1 maps using a maximum likelihood estimator approach},
	year = {2013},
	doi = {10.3389},
	author = {Gabriel Ramos-Llord{\'e}n and Jan Sijbers}
}
@inproceedings {1439,
	title = {A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images},
	booktitle = {5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings},
	volume = {8251},
	year = {2013},
	month = {2013},
	edition = {Lecture Notes in Computer Science},
	doi = {10.1007/978-3-642-45062-4_62},
	author = {Jeny Rajan and Arnold Jan den Dekker and Juntu, Jaber and Jan Sijbers}
}
@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}
}
@inproceedings {1332,
	title = {An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images},
	booktitle = { IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2012},
	author = {Jeny Rajan and Johan Van Audekerke and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@conference {1415,
	title = {DTI-based Classification of Affection Status in Asperger Syndrome.},
	year = {2012},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Sams, Mikko}
}
@mastersthesis {1378,
	title = {Estimation and removal of noise from single and multiple coil  Magnetic Resonance images},
	year = {2012},
	month = {11/2012},
	type = {PhD Thesis},
	author = {Jeny Rajan}
}
@article {1515,
	title = {Extraction of airways from CT (EXACT{\textquoteright}09).},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {31},
	year = {2012},
	month = {2012 Nov},
	pages = {2093-107},
	abstract = {This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74\% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.},
	keywords = {Algorithms, Analysis of Variance, Databases, Factual, Humans, Lung, Radiographic Image Enhancement, Tomography, X-Ray Computed, Trachea},
	issn = {1558-254X},
	doi = {10.1109/TMI.2012.2209674},
	author = {Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and de Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and R{\^o}mulo Pinho and Jan Sijbers and Feuerstein, Marco and Fabija{\'n}ska, Anna and Bauer, Christian and Beichel, Reinhard and Mendoza, Carlos S and Wiemker, Rafael and Lee, Jaesung and Reeves, Anthony P and Born, Silvia and Weinheimer, Oliver and van Rikxoort, Eva M and Tschirren, Juerg and Mori, Ken and Odry, Benjamin and Naidich, David P and Hartmann, Ieneke and Eric A. Hoffman and Prokop, Mathias and Pedersen, Jesper H and de Bruijne, Marleen}
}
@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}
}
@article {1309,
	title = {Microstructural changes observed with DKI in a transgenic Huntington rat model: Evidence for abnormal neurodevelopment.},
	journal = {NeuroImage},
	volume = {59},
	number = {2},
	year = {2012},
	month = {2012 Jan 16},
	pages = {957-67},
	abstract = {Huntington Disease (HD) is a fatal neurodegenerative disorder, caused by a mutation in the Huntington gene. Although HD is most often diagnosed in mid-life, the key to its clinical expression may be found during brain maturation. In the present work, we performed in vivo diffusion kurtosis imaging (DKI) in order to study brain microstructure alterations in developing transgenic HD rat pups. Several developing brain regions, relevant for HD pathology (caudate putamen, cortex, corpus callosum, external capsule and anterior commissure anterior), were examined at postnatal days 15 (P15) and 30 (P30), and DKI results were validated with histology. At P15, we observed higher mean (MD) and radial (RD) diffusivity values in the cortex of transgenic HD rat pups. In addition, at the age of P30, lower axial kurtosis (AK) values in the caudate putamen of transgenic HD pups were found. At the level of the external capsule, higher MD values at P15 but lower MD and AD values at P30 were detected. The observed DKI results have been confirmed by myelin basic protein immunohistochemistry, which revealed a reduced fiber staining as well as less ordered fibers in transgenic HD rat pups. These results indicate that neuronal development in young transgenic HD rat pups occurs differently compared to controls and that the presence of mutant huntingtin has an influence on postnatal brain development. In this context, various diffusivity parameters estimated by the DKI model are a powerful tool to assess changes in tissue microstructure and detect developmental changes in young transgenic HD rat pups.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2011.08.062},
	author = {Ines Blockx and G. De Groof and Marleen Verhoye and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and Osmand, Alexander P and Von H{\"o}rsten, Stephan and Annemie Van Der Linden}
}
@article {1438,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.},
	journal = {Magnetic resonance imaging},
	volume = {30},
	year = {2012},
	month = {2012 Dec},
	pages = {1512-8},
	abstract = {Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.},
	keywords = {Algorithms, Animals, Brain, Brain Mapping, Computer Simulation, Fourier Analysis, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Mice, Models, Statistical, Normal Distribution, Signal-To-Noise Ratio, Stochastic Processes},
	issn = {1873-5894},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1344,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images},
	journal = {Magnetic Resonance Imaging},
	volume = {30},
	year = {2012},
	pages = {1512-1518},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {1358,
	title = {Visualizing the Segmentation Error of a Tomogram using the Residual Projection Error},
	booktitle = {The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting)},
	year = {2012},
	pages = {293-296},
	address = {Salt Lake City, UT, USA},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@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 {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 {1245,
	title = {Denoising magnitude MRI using an adaptive NLML method},
	year = {2011},
	pages = {383},
	address = {Leipzig, Germay},
	author = {Jeny Rajan and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@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 {1306,
	title = {Denoising SENSE reconstructed MR images},
	year = {2011},
	author = {Jeny Rajan and Jan Sijbers}
}
@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 {1331,
	title = {An extended NLML method for denoising non-central chi distributed data - application to parallel MRI},
	year = {2011},
	pages = {41},
	author = {Jeny Rajan and Johan Van Audekerke and Jelle Veraart and Marleen Verhoye and Jan Sijbers}
}
@article {jrajanbjeurissmverhoyeJ.jsijbers2011,
	title = {Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods},
	journal = {Physics in Medicine and Biology},
	volume = {56},
	number = {16},
	year = {2011},
	pages = {5221-5234},
	doi = {doi:10.1088/0031-9155/56/16/009},
	author = {Jeny Rajan and Ben Jeurissen and Marleen Verhoye and Johan Van Audekerke and Jan Sijbers}
}
@inproceedings {jrajanmverhoyejsijbers2011,
	title = {A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood},
	booktitle = {SPIE Medical Imaging},
	volume = {7962},
	year = {2011},
	publisher = {SPIE},
	organization = {SPIE},
	author = {Jeny Rajan and Marleen Verhoye and Jan Sijbers},
	editor = {B.M. Dawant and D.R. Haynor}
}
@inproceedings {1194,
	title = {PDART: A Partially Discrete Algorithm for the Reconstruction of Dense Particles},
	booktitle = {11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D)},
	year = {2011},
	pages = {448-451},
	address = {Potsdam, Germany},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@article {P.M.A.S.bjeurissJ.A.M.K.F.T.J.-F.C.2011,
	title = {Quantitative Evaluation of 10 Tractography Algorithms on a Realistic Diffusion MR Phantom},
	journal = {NeuroImage},
	volume = {56},
	number = {1},
	year = {2011},
	month = {May},
	pages = {220-234},
	author = {Pierre Fillard and Maxime Descoteaux and Alvina Goh and Sylvain Gouttard and Ben Jeurissen and James Malcolm and Alonso Ramirez-Manzanares and Marco Reisert and Ken Sakaie and Fatima Tensaouti and Ting-Shou Yo and Jean-Fran{\c c}ois Mangin and Cyril Poupon}
}
@conference {1256,
	title = {Robust edge directed interpolation of diffusion weighted MR images},
	year = {2011},
	pages = {382},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@conference {zmaijrajanmverhoyejsijbers2011,
	title = {Robust Edge-directed Interpolation: Application to Diffusion MR Images},
	year = {2011},
	month = {May},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@article {1261,
	title = {Robust edge-directed interpolation of magnetic resonance images},
	journal = {Physics in medicine and biology},
	volume = {56},
	year = {2011},
	pages = {7287-7303},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye 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}
}
@conference {1204,
	title = {Ultra-High Resolution Electron Tomography for Materials Science: A Roadmap},
	year = {2011},
	address = {Nashville, TN, United States},
	doi = {http://dx.doi.org/10.1017/S143192761100554X},
	author = {Kees Joost Batenburg and Sara Bals and Sandra Van Aert and Roelandts, Tom and Jan Sijbers}
}
@article {jjuntujsijberssdbackerjrajandvandyck2010,
	title = {A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {31},
	year = {2010},
	pages = {680{\textendash}689},
	doi = {10.1002/jmri.22095},
	author = {Juntu, Jaber and Jan Sijbers and Steve De Backer and Jeny Rajan and Dirk Van Dyck}
}
@article {1189,
	title = {Noise measurement from magnitude MRI using local estimates of variance and skewness.},
	journal = {Physics in medicine and biology},
	volume = {55},
	year = {2010},
	month = {2010 Aug 21},
	pages = {N441-9},
	abstract = {In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.},
	keywords = {Algorithms, Artifacts, Brain, Data Interpretation, Statistical, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Models, Statistical, Myocardium, Normal Distribution, Reproducibility of Results},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/55/16/N02},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@conference {1203,
	title = {Partially Discrete Tomography for the Reconstruction of Dense Particles},
	year = {2010},
	pages = {I7.19},
	address = {Rio de Janeiro, Brazil},
	author = {Roelandts, Tom and Kees Joost Batenburg and E. Biermans and Sara Bals and Jan Sijbers}
}
@inproceedings {DBLP:conf/iciar/RajanPJS10,
	title = {Segmentation Based Noise Variance Estimation from Background MRI Data},
	booktitle = {ICIAR },
	volume = {6111},
	year = {2010},
	month = {2010},
	pages = {62-70},
	publisher = {Springer LNCS},
	organization = {Springer LNCS},
	address = {Porto, Portugal},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@article {1268,
	title = {X-ray micro tomography and image analysis as complementary methods for morphological characterization and coating thickness measurement of coated particles},
	journal = {Advanced Powder Technology},
	volume = {21},
	year = {2010},
	month = {11/2010},
	pages = {663 - 675},
	issn = {09218831},
	doi = {10.1016/j.apt.2010.08.002},
	author = {Perfetti, Giacomo and Elke Van de Casteele and Rieger, Bernd and Wildeboer, Willem J. and Meesters, Gabrie M.H.}
}
@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}
}
@conference {BlockxmverhoyeGroofAudekerkeRaberdpootjsijbersHorstenavdlinde2009,
	title = {Diffusion Kurtosis Imaging (DKI) reveals an early phenotype (P30) in a transgenic rat model for Huntington{\textquoteright}s disease},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {359},
	author = {Ines Blockx and Marleen Verhoye and G. De Groof and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and S. von Horsten and Annemie Van Der Linden}
}
@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 {1374,
	title = {Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited.},
	journal = {Human brain mapping},
	volume = {30},
	year = {2009},
	month = {2009 Nov},
	pages = {3657-75},
	abstract = {Voxel-based analyses (VBA) are increasingly being used to detect white matter abnormalities with diffusion tensor imaging (DTI) in different types of pathologies. However, the validity, specificity, and sensitivity of statistical inferences of group differences to a large extent depend on the quality of the spatial normalization of the DTI images. Using high-dimensional nonrigid coregistration techniques that are able to align both the spatial and orientational diffusion information and incorporate appropriate templates that contain this complete DT information may improve this quality. Alternatively, a hybrid technique such as tract-based spatial statistics (TBSS) may improve the reliability of the statistical results by generating voxel-wise statistics without the need for perfect image alignment and spatial smoothing. In this study, we have used (1) a coregistration algorithm that was optimized for coregistration of DTI data and (2) a population-based DTI atlas to reanalyze our previously published VBA, which compared the fractional anisotropy and mean diffusivity maps of patients with amyotrophic lateral sclerosis (ALS) with those of healthy controls. Additionally, we performed a complementary TBSS analysis to improve our understanding and interpretation of the VBA results. We demonstrate that, as the overall variance of the diffusion properties is lowered after normalizing the DTI data with such recently developed techniques (VBA using our own optimized high-dimensional nonrigid coregistration and TBSS), more reliable voxel-wise statistical results can be obtained than had previously been possible, with our VBA and TBSS yielding very similar results. This study provides support for the view of ALS as a multisystem disease, in which the entire frontotemporal lobe is implicated.},
	keywords = {Adult, Aged, Algorithms, Amyotrophic Lateral Sclerosis, anisotropy, Brain, Brain Mapping, Case-Control Studies, diffusion tensor imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Reproducibility of Results, Sensitivity and Specificity, Statistics as Topic},
	issn = {1097-0193},
	doi = {10.1002/hbm.20794},
	author = {Caroline A Sage and Wim Van Hecke and Ron R Peeters and Jan Sijbers and Robberecht, Wim and Paul M Parizel and Marchal, Guy and Alexander Leemans and Stefan Sunaert}
}
@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 {evdcastedvandyckjsijberseraman2004,
	title = {The effect of beam hardening on MTF measurements in X-ray microtomography},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {evdcastedvandyckjsijberseraman2004,
	title = {Effect of beam hardening on resolution in x-ray microtomography},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {5370},
	year = {2004},
	month = {February},
	pages = {2089-2096},
	address = {San Diego CA, USA},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman},
	editor = {Milan Sonka}
}
@mastersthesis {1289,
	title = {Model-based approach for Beam Hardening Correction and Resolution Measurements in Microtomography},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Elke Van de Casteele}
}
@article {evdcastedvandyckjsijberseraman2004,
	title = {A model-based correction method for beam hardening artefacts in X-ray microtomography},
	journal = {Journal of X-ray science and technology},
	volume = {12},
	number = {1},
	year = {2004},
	pages = {53-57},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@mastersthesis {1285,
	title = {On the wavefront aberrations of the human eye and the search for their origins},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Jos Rozema}
}
@inproceedings {1292,
	title = {A bimodal energy model for correcting beam hardening artefacts in X-ray tomography},
	booktitle = {IEEE 29th Annual Northeast Bioengineering Conference2003 IEEE 29th Annual Proceedings of Bioengineering Conference},
	year = {2003},
	pages = {57 - 58},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Newark, NJ, USA},
	doi = {10.1109/NEBC.2003.1215990},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {wdhaesxrodet2003,
	title = {Discrete Cepstrum Coefficients as Perceptual Features},
	booktitle = {International Computer Music Conference (ICMC)},
	year = {2003},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@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 {evdcastedvandyckjsijberseraman2003,
	title = {A model-based correction method for beam-hardening artifacts in X-ray tomography},
	booktitle = {22nd Benelux Meeting on Systems and Control},
	year = {2003},
	month = {March},
	address = {Lommel, Belgium},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {evdcastedvandyckjsijberseraman2003,
	title = {Modelling of Beam hardening in micro CT},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2003},
	month = {July},
	pages = {57-58},
	address = {Washington DC, USA},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {wdhaesxrodet2003,
	title = {A New Estimation Technique for Determining the Control Parameters of a physical model of a trumpet},
	booktitle = {6th International Conference on Digital Audio Effects (DAFx03), London, England},
	year = {2003},
	month = {September},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@article {wdhaesdvandyckxrodet2003,
	title = {PCA-based Branch and Bound Search Algorithms for Computing K Nearest Neighbors},
	journal = {Pattern Recognition Letters},
	volume = {24},
	year = {2003},
	pages = {1437-1451},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@inproceedings {wdhaesdvandyckxrodet2002,
	title = {Control Parameter Estimation for a Physical Model of a Trumpet Using Pattern Recognition},
	booktitle = {IEEE Workshop on Model Based Processing and Coding of Audio (MPCA), Leuven, Belgium},
	year = {2002},
	month = {November},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@inproceedings {wdhaesdvandyckxrodet2002,
	title = {An efficient branch and bound seach algorithm for computing K nearest neighbors in a multidimensional vector space},
	booktitle = {IEEE Advanced Concepts for Intelligent Vision Systems (ACIVS), Gent, Belgium},
	year = {2002},
	month = {September},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@inproceedings {wdhaesdvandyckxrodet2002,
	title = {An efficient branch and bound seach algorithm for computing K nearest neighbors in a multidimensional vector space},
	booktitle = {Signal Processing, Pattern Recognition and Applications (SPPRA), Crete, Greece},
	year = {2002},
	month = {June},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@article {evdcastedvandyckjsijberseraman2002,
	title = {An energy-based beam hardening model in tomography},
	journal = {Physics in Medicine and Biology},
	volume = {47},
	number = {23},
	year = {2002},
	pages = {4181-4190},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {wdhaesxrodet2002,
	title = {Physical Constraints for the Control of a Physical Model of a Trumpet},
	booktitle = {International Conference on Digital Audio Effects (DAFx), Hamburg, Germany},
	year = {2002},
	month = {September},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@inproceedings {wdhaesxrodet2001,
	title = {Automatic Estimation of Control Parameters: An Instance-Based Learning Approach},
	booktitle = {International Computer Music Conference (ICMC), Havana, Cuba},
	year = {2001},
	month = {September},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic Feature Selection Combined with Fuzzy K-NN for Hyperspectral Satellite Imagery},
	booktitle = {Intelligent Techniques and Soft Computing in Nuclear Science and Engineering},
	year = {2000},
	pages = {281-288},
	publisher = {World Scientific},
	organization = {World Scientific},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders},
	editor = {d. Ruan, H.A. Abderrahim, P. D{\textquoteright}Hondt, E. Kerre}
}
@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 {jsijbersajdendekeramandvandyck1999,
	title = {Parameter estimation from magnitude MR images},
	journal = {International Journal of Imaging Systems and Technology},
	volume = {10},
	number = {2},
	year = {1999},
	pages = {109-114},
	doi = {10.1002/(SICI)1098-1098(1999)10:2<109::AID-IMA2>3.0.CO;2-R},
	author = {Jan Sijbers and Arnold Jan den Dekker and Erik Raman and Dirk Van Dyck}
}
@mastersthesis {1302,
	title = {Estimation of Signal and Noise from Magnitude Magnetic Resonance Images},
	year = {1998},
	type = {PhD thesis},
	author = {Jan Sijbers}
}
@inproceedings {jsijbersajdendekdvandyckeraman1998,
	title = {Estimation of signal and noise from Rician distributed data},
	booktitle = {Proceedings of the IASTED International Conference on Signal Processing and Communications},
	year = {1998},
	month = {February},
	pages = {140-143},
	address = {Canary Islands, Spain},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk Van Dyck and Erik Raman}
}
@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}
}
@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 {jsijberspscheunddvandyckeraman1997,
	title = {Noise quantification prior to image restoration},
	booktitle = {Meeting of the Dutch Society for Pattern Recognition and Image Processing},
	year = {1997},
	month = {May},
	address = {Best, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inbook {dvandyckebettensjsijbersajdendekBosBeeckJansenZandbergen1997,
	title = {Resolving atoms: what do we have? what do we want?},
	booktitle = {Institute of Physics Conference Series},
	volume = {153},
	number = {3},
	year = {1997},
	month = {December},
	pages = {95-100},
	publisher = {Institute of Physics Ltd},
	organization = {Institute of Physics Ltd},
	address = {Cambridge, UK},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and Arnold Jan den Dekker and A. van den Bos and M. Op de Beeck and J. Jansen and H. Zandbergen},
	editor = {J. M. Rodenburg}
}
@inproceedings {jsijbersajdendekpscheunderamandvandyck1997,
	title = {Unbiased signal estimation in magnitude MR images},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {174},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Erik Raman and Dirk Van Dyck}
}
@article {jsijbersmverhoyepscheundavdlindedvandyckeraman1997,
	title = {Watershed based segmentation of 3D MR data for volume quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {15},
	number = {6},
	year = {1997},
	pages = {679-688},
	doi = {10.1016/S0730-725X(97)00033-7},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Dirk Van Dyck and Erik Raman}
}
@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}
}
@article {jsijberspscheundBonnetdvandyckeraman1996,
	title = {Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure},
	journal = {Magnetic Resonance Imaging},
	volume = {14},
	number = {10},
	year = {1996},
	pages = {1157-1163},
	doi = {10.1016/S0730-725X(96)00219-6},
	author = {Jan Sijbers and Paul Scheunders and N. Bonnet and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersmverhoyepscheundavdlindeAudekerkedvandyckeraman1996,
	title = {Segmentation Scheme for Volume Quantization from 3D Microscopic MR images},
	booktitle = {Proceedings of the Joint Meeting NVvM \& BVM},
	year = {1996},
	month = {December},
	pages = {168-169},
	address = {Gent},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Johan Van Audekerke and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersavdlindepscheundAudekerkeeramandvandyck1996,
	title = {Semi-automatic mouse cerebellum extraction from 3D magnetic resonance data},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	volume = {3},
	year = {1996},
	month = {April},
	pages = {1585},
	address = {New York, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Erik Raman and Dirk Van Dyck}
}
@inproceedings {jsijbersavdlindepscheundAudekerkedvandyckeraman1996,
	title = {Volume quantization of the mouse cerebellum by semi-automatic 3D segmentation of MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {2710},
	year = {1996},
	month = {February},
	pages = {553-560},
	address = {Newport Beach CA, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Dirk Van Dyck and Erik Raman},
	editor = {H. Loew and Kenneth M. Hanson}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {Proceedings of the Royal Microscopical Society},
	booktitle = {Optimization of the SNR in NMR images using image sequences},
	volume = {29},
	number = {4},
	year = {1994},
	month = {September},
	pages = {232},
	address = {London, UK},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {The use of two NMR realizations for determining and improving of the image SNR},
	booktitle = {Proceedings of the Joint Meeting of the Belgian and Dutch Societies for Electron Microscopy},
	year = {1994},
	month = {May},
	pages = {65},
	address = {Arnhem, Papendal, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberseramanAudekerkeMalflietLindenVerhoyeCloeck1993,
	title = {The construction of a gradient set},
	booktitle = {4th Meeting of the FGWO/FRSM Contact Group Biomedical MR},
	year = {1993},
	pages = {5},
	address = {Louvain-la-Neuve, Belgium},
	author = {Jan Sijbers and Erik Raman and Johan Van Audekerke and W. Malfliet and Annemie Van Der Linden and Marleen Verhoye and C. Cloeck}
}
