@article {2436,
	title = {Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening},
	journal = {Frontiers in Immunology},
	volume = {17},
	year = {2026},
	doi = {https://doi.org/10.3389/fimmu.2026.1625837},
	author = {Sofie Aerts and Lorin Werthen-Brabants and Hamza Khan and Diana Giraldo and Edward De Brouwer and Lotte Geys and Veronica Popescu and Jan Sijbers and Henry C. Woodruff and Tom Dhaene and Dirk Deschrijver and Bart Van Wijmeersch and Philippe Lambin and Liesbet M. Peeters}
}
@article {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 {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 {2457,
	title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3{\textemdash}Ex vivo imaging: Data processing, comparisons with microscopy, and tractography},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2561 - 2582},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30424},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30424},
	author = {Schilling, Kurt G. and Howard, Amy F. D. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@article {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 {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 {2383,
	title = {Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-024-01147-9},
	author = {Miroslav Yosifov and Thomas Lang and Virginia Florian and Stefan Gerth and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@article {2406,
	title = {Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q MINDED Brain Intra-and Inter-Scanner Study},
	journal = {Frontiers in Neuroscience},
	volume = {19},
	year = {2025},
	doi = {10.3389/fnins.2025.1591169},
	author = {Maira Siqueira Pinto and Vincenzo Anania and Roberto Paolella and Celine Smekens and Thibo Billiet and Thomas Janssens and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck}
}
@mastersthesis {2430,
	title = {Improved Diagnosis and Prognosis of Mild Traumatic Brain Injury via Harmonised Diffusion MRI},
	year = {2025},
	type = {PhD thesis},
	author = {Maira Siqueira Pinto}
}
@article {2426,
	title = {Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Worsening in People with Multiple Sclerosis},
	journal = {Front. Neurosci.},
	volume = {19},
	year = {2025},
	doi = {https://doi.org/10.3389/fnins.2025.1610401},
	author = {Hamza Khan and Diana Giraldo and Henry C. Woodruff and Lorin Werthen-Brabants and Shruti Atul Mali and Sina Amirrajab and Edward De Brouwer and Veronica Popescu and Bart Van Wijmeersch and Oliver Gerlach and Jan Sijbers and Liesbet M. Peeters and Philippe Lambin}
}
@article {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}
}
@mastersthesis {2416,
	title = {Shaping the future: 3D knee morphology analysis and predictive models for data-driven patient selection},
	volume = {PhD in Medicine and Health Sciences},
	year = {2025},
	type = {PhD thesis},
	author = {Jonas Grammens}
}
@article {2343,
	title = {Automated landmark annotation for morphometric analysis of distal femur and proximal tibia},
	journal = {Journal of Imaging},
	volume = {10},
	year = {2024},
	doi = {10.3390/jimaging10040090 },
	author = {Jonas Grammens and Annemieke Van Haver and Imelda Lumban-Gaol and Femke Danckaers and Peter Verdonk 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 {2364,
	title = {Perceptual super-resolution in multiple sclerosis MRI},
	journal = {Frontiers in Neuroscience},
	volume = {18},
	year = {2024},
	doi = {10.3389/fnins.2024.1473132},
	author = {Diana Giraldo and Hamza Khan and Gustavo Pineda and Liang, Zhihua and Alfonso Lozano and Bart Van Mijweersch and Henry C. Woodruff and Philippe Lambin and Romero, Eduardo and Liesbet M. Peeters and Jan Sijbers}
}
@article {2296,
	title = {A preparation pulse for fast steady state approach in Actual Flip angle Imaging},
	journal = {Medical Physics},
	volume = {51},
	year = {2024},
	pages = {306-318},
	doi = {https://doi.org/10.1002/mp.16624},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@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}
}
@article {2355,
	title = {Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study},
	journal = {Journal of Experimental Orthopaedics},
	volume = {11},
	year = {2024},
	pages = {e12090},
	doi = {https://doi.org/10.1002/jeo2.12090},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Kristien Vuylsteke and Jan Sijbers and Lotem Mahluf and Peter Angele and Elizaveta Kon and Peter Verdonk}
}
@inproceedings {2441,
	title = {Towards dopaminergic SPECT estimation from structural MRI sequences to characterize Parkinson{\textquoteright}s disease},
	booktitle = {2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
	year = {2024},
	pages = {1-4},
	keywords = {Codes, Diseases, Engineering in medicine and biology, Estimation, Magnetic Resonance Imaging, Magnetic resonance imaging (MRI), Motors, Neurons, non-aligned projections, Parkinson{\textquoteright}s disease, Proposals, Resonance, self-supervised learning, Single photon emission computed tomography, Single-Photon Emission Computed Tomography (SPECT)},
	doi = {10.1109/EMBC53108.2024.10782073},
	author = {Sierra-Jerez, Franklin and Valencia, Yeferson and Diana Giraldo and Mart{\'\i}nez, Fabio}
}
@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 {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}
}
@conference {2300,
	title = {Exploring the Correlation between Disability Status and Brain Volumetric Measurements Using Real-World Retrospective Magnetic Resonance Images in People with Multiple Sclerosis},
	number = {1637},
	year = {2023},
	address = {October 11-13, Milan, Italy},
	abstract = {This study investigated whether retrospective real-world MR image databases can be used to obtain MR-based biomarkers of multiple sclerosis (MS) disability by examining correlations between Expanded Disability Status Scale (EDSS) scores and volumetric measurements from reconstructed T2-weighted fluid-attenuated inversion recovery (FLAIR) in people with MS (PwMS).},
	author = {Hamza Khan and Diana Giraldo and Quinten Beirinckx and Jan Sijbers and Philippe Lambin and Henry C. Woodruff and Liesbet M. Peeters}
}
@inproceedings {2317,
	title = {An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders},
	booktitle = { IEEE International Geoscience and Remote Sensing Symposium},
	year = {2023},
	month = {20 October 2023},
	pages = {5890-5893},
	abstract = {Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain.In this work, we generated a comprehensive hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets)},
	doi = {10.1109/IGARSS52108.2023.10281467},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2324,
	title = {A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures},
	journal = {IEEE Sensors Journal},
	year = {2023},
	month = {2023/12/28},
	abstract = {Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. 

In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets.},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Sandra Lorenz and Margret Fuchs and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@inproceedings {2288,
	title = {Super-resolution reconstruction of multi-slice T2-w FLAIR MRI improves Multiple Sclerosis lesion segmentation},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	address = {Sydney, Australia},
	abstract = {Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and posterior lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.},
	author = {Diana Giraldo and Quinten Beirinckx and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@article {2403,
	title = {Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging},
	journal = {RSC Advances},
	volume = {12},
	year = {2022},
	pages = {32775-32783},
	author = {Thomas De Kerf and Arthur Gestels and Janssens, Koen and Gunther Steenackers and Paul Scheunders and Steve Vanlanduit}
}
@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}
}
@inproceedings {2176,
	title = {2D/3D registration with a statistical deformation model prior using deep learning},
	booktitle = {the IEEE International Conference on Biomedical and Health Informatics (BHI{\textquoteright}21) },
	year = {2021},
	pages = {1-4},
	doi = {10.1109/BHI50953.2021.9508540},
	author = {Jeroen Van Houtte and Xiaoru Gao and Jan Sijbers and Guoyan Zheng}
}
@article {2079,
	title = {Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing},
	journal = {Magnetic Resonance in Medicine},
	volume = {85},
	year = {2021},
	pages = {1397-1413},
	doi = {https://doi.org/10.1002/mrm.28520},
	author = {Maarten Naeyaert and Jan Aelterman and Vladimir Golkov and Daniel Cremers and Aleksandra Pizurica and Jan Sijbers and Marleen Verhoye}
}
@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}
}
@conference {2183,
	title = {EPIFANI for ultrafast B1-corrected T1 and PD mapping},
	volume = {34},
	year = {2021},
	pages = {S59-S60},
	doi = {10.1007/s10334-021-00947-8},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@conference {2147,
	title = {Faster and better HARDI using FSE and holistic reconstruction},
	year = {2021},
	author = {Maarten Naeyaert and Vladimir Golkov and Daniel Cremers and Jan Sijbers and Marleen Verhoye}
}
@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 {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}
}
@conference {2174,
	title = {Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study},
	year = {2021},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@conference {2185,
	title = {Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI},
	volume = {34},
	year = {2021},
	pages = {S54},
	doi = {10.1007/s10334-021-00947-8},
	author = {Maira Siqueira Pinto and Stefan Winzeck and S. Richter and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Pieter-Jan Guns and Arnold Jan den Dekker and Jan Sijbers and Virginia F. J. Newcombe and Pieter Van Dyck}
}
@article {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}
}
@conference {2173,
	title = {RAMSES: Relaxation Alternate Mapping of Spoiled Echo Signals sequence for simultaneous accurate T1 and T2* mapping},
	year = {2021},
	month = {May 2021},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@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 {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}
}
@article {2063,
	title = {Atom column detection from simultaneously acquired ABF and ADF STEM images},
	journal = {Ultramicroscopy},
	volume = {219},
	year = {2020},
	pages = {113046},
	doi = {https://doi.org/10.1016/j.ultramic.2020.113046},
	author = {Fatermans, J. and Arnold Jan den Dekker and M{\"u}ller-Caspary, K. and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@conference {2091,
	title = {Bayesian model selection for atom column detection from ABF-ADF STEM images},
	year = {2020},
	author = {Fatermans, J. and Arnold Jan den Dekker and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@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}
}
@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}
}
@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}
}
@article {2070,
	title = {Small medial femoral condyle morphotype is associated with medial compartment degeneration and distinct morphological characteristics: a comparative pilot study},
	journal = {Knee Surgery, Sports Traumatology, Arthroscopy},
	year = {2020},
	doi = {https://doi.org/10.1007/s00167-020-06218-8},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Brian G Booth and Jan Sijbers and Peter Verdonk}
}
@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 {1962,
	title = {Cognitive Training in Young Patients With Traumatic Brain Injury: A Fixel-Based Analysis.},
	journal = {Neurorehabil Neural Repair},
	year = {2019},
	month = {2019 Aug 16},
	abstract = {Traumatic brain injury (TBI) is associated with altered white matter organization and impaired cognitive functioning.
We aimed to investigate changes in white matter and cognitive functioning following computerized cognitive training.
Sixteen adolescents with moderate-to-severe TBI (age 15.6 {\textpm} 1.8 years, 1.2-4.6 years postinjury) completed the 8-week BrainGames program and diffusion weighted imaging (DWI) and cognitive assessment at time point 1 (before training) and time point 2 (after training). Sixteen healthy controls (HC) (age 15.6 {\textpm} 1.8 years) completed DWI assessment at time point 1 and cognitive assessment at time point 1 and 2. Fixel-based analyses were used to examine fractional anisotropy (FA), mean diffusivity (MD), and fiber cross-section (FC) on a whole brain level and in tracts of interest. 
Patients with TBI showed cognitive impairments and extensive areas with decreased FA and increased MD together with an increase in FC in the body of the corpus callosum and left superior longitudinal fasciculus (SLF) at time point 1. Patients improved significantly on the inhibition measure at time point 2, whereas the HC group remained unchanged. No training-induced changes were observed on the group level in diffusion metrics. Exploratory correlations were found between improvements on verbal working memory and reduced MD of the left SLF and between increased performance on an information processing speed task and increased FA of the right precentral gyrus.

Results are indicative of positive effects of BrainGames on cognitive functioning and provide preliminary evidence for neuroplasticity associated with cognitive improvements following cognitive intervention in TBI.},
	issn = {1552-6844},
	doi = {10.1177/1545968319868720},
	author = {Verhelst, Helena and Diana Giraldo and Vander Linden, Catharine and Vingerhoets, Guy and Ben Jeurissen and Caeyenberghs, Karen}
}
@inproceedings {1932,
	title = {A Comparison Between Representative 3D Faces Based on Bi- and Multi-variate and Shape Based Analysis},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)- Volume VII},
	volume = {VII},
	year = {2019},
	pages = {1355{\textendash}1364},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {In Ergonomic product design, designers need to translate anthropometric data of the target population into product dimensions or sizing systems. Currently, sizing systems are often based on traditional anthropometric data and generally use the variation of one or two key body dimensions directly related to the product. For products that need to closely fit a certain part of the body it is relevant to incorporate multiple key dimensions. This can be realized by a multivariate approach such as a Principal Component Analysis. Over the past decades, there has been an increase in incorporating 3D imaging in anthropometric surveys. In order to integrate the use of 3D anthropometry in product sizing, representative models are used to visualize the variability of the target population. For the development of a ventilation mask for children, this study compares representative models of 3D faces based on a bivariate, multivariate and shape based analysis of 303 children{\textquoteright}s faces.},
	keywords = {3D anthropometry, Children, Design, Product sizing, Ventilation mask},
	isbn = {978-3-319-96070-8},
	doi = {10.1007/978-3-319-96071-5_137},
	author = {Ly{\`e} Goto and Toon Huysmans and Wonsup Lee and Molenbroek, Johan F. M. and Richard Goossens}
}
@inproceedings {1918,
	title = {An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	author = {Johannes Weissenb{\"o}ck and Bernhard Fr{\"o}hler and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Santhosh Ayalur Karunakaran and Helmuth Hoeller and Johann Kastner and Christoph Heinzl}
}
@article {1987,
	title = {A supervised method for nonlinear hyperspectral unmixing},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	month = {10/2019},
	abstract = {Due to the complex interaction of light with the Earth surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occuring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning the nonlinear mapping, based on gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset show that this novel methodology is very promising.},
	doi = {https://doi.org/10.3390/rs11202458},
	url = {https://www.mdpi.com/2072-4292/11/20/2458},
	author = {Bikram Koirala and Mahdi Khodadadzadeh and Cecilia Contreras and Zohreh Zahiri and Richard Gloaguen and Paul Scheunders}
}
@inproceedings {1917,
	title = {Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_52.pdf},
	author = {Bernhard Fr{\"o}hler and Lucas da Cunha Melo and Johannes Weissenb{\"o}ck and Johann Kastner and Torsten M{\"o}ller and Hans-Christian Hege and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Christoph Heinzl}
}
@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}
}
@inproceedings {1933,
	title = {Capturing Joint Angles of the Off-Site Human Body},
	booktitle = {IEEE Sensors 2018},
	year = {2018},
	pages = {1{\textendash}4},
	publisher = {IEEE},
	organization = {IEEE},
	address = {United States},
	abstract = {Motion capture (mocap) is traditionally conducted by optical systems. These are expensive and usually limited to controlled environments. We investigated the accuracy of portable and inexpensive mocap sensor systems compared to benchmark optical systems with respect to tracking joint angles. This review summarizes the findings of 21 studies. In these studies, 228 subjects were employed, and 16 joints were tracked, spanning a range of activities. We did not find a system that is consistent and equally accurate across all joint angles for all activities (root mean square error up to 12.1 degrees). However, under some ideal conditions, the results are on par with optical mocap. Our recommendations for future research and development are to focus on tracking faster activities, activities in off-site conditions, and following standardized biomechanical models of joint angles.},
	keywords = {IMU, joint kinematics, motion capture, off-site, wearable},
	isbn = {978-1-5386-4708-0},
	doi = {10.1109/ICSENS.2018.8589711},
	author = {Raman Garimella and Thomas Peeters and Koen Beyers and Steven Truijen and Toon Huysmans and Stijn Verwulgen}
}
@inproceedings {2005,
	title = {Classification of hardened cement and lime mortar using short-wave infrared spectrometry data},
	booktitle = {11th international conference on Structural Analysis of Historical Constructions},
	year = {2018},
	abstract = {This paper evaluated the feasibility of using spectrometry data in the short-wave infrared range (1,300-2,200 nm) two distinguish lime mortar and Type S cement mortar. 42 samples of 40x40x40mm were created in the lab (21 lime-based, 21 cement-based). A Partial Least Square Discriminant Analysis model was developed using the mean spectra of 28 specimens as the calibration set. The results were tested on the mean spectra of the remaining 14 specimens as a validation set. The results showed that spectrometry data were able to fully distinguish modern mortars (made with cement) from historic lime mortars with a 100\% classification accuracy, which can be very useful in archaeological and architectural conservation applications. Specifically, being able to distinguish mortar composition in situ can provide critical information about the construction history of a structure as well as to inform an appropriate intervention scheme when historic material needs to be repaired or replaced.},
	author = {Zohreh Zahiri and Debra Laefer and Aoife Gowen}
}
@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 {1817,
	title = {Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid β-induced pathology.},
	journal = {Alzheimer{\textquoteright}s Research \& Therapy },
	volume = {10},
	year = {2018},
	pages = {1-16},
	doi = {10.1186/s13195-017-0329-8},
	author = {Jelle Praet and Nikolay Manyakov and Leacky Muchene and Zhenhua Mai and Vasilis Terzopoulos and Steve De Backer and An Torremans and Pieter-Jan Guns and Tom Van De Casteele and Astrid Bottelbergs and Bianca Van Broeck and Jan Sijbers and Dirk Smeets and Ziv Shkedy and Luc Bijnens and Darrel Pemberton and Mark Schmidt and Annemie Van Der Linden and Marleen Verhoye}
}
@article {1872,
	title = {Enhanced contrast in X-ray microtomographic images of the membranous labyrinth using different X-ray sources and scanning modes},
	journal = {Journal of Anatomy},
	volume = {233},
	year = {2018},
	pages = {770--782},
	doi = {doi: 10.1111/joa.12885},
	author = {Jana Goyens and Menelia Vasilopoulou-Kampitsi and Raf Claes and Jan Sijbers and Lucia Mancini}
}
@article {2004,
	title = {The feasibility of short-wave infrared spectrometry in assessing water-to-cement ratio and density of hardened concrete},
	journal = {Construction and Building Materials},
	year = {2018},
	abstract = {This paper describes the feasibility of using short-wave infrared (SWIR) spectrometry to classify concretes by their water-to-cement (w/c) ratios and predict their density. Concrete spectra of three w/c ratios (50\%, 65\%, 80\%) were studied in the 1300{\textendash}2200 nm range. A Partial Least Square Discriminant Analysis model was developed from the spectra of 36 samples, resulting in an 89\% correct classification for the 18 validation samples, thereby demonstrating that SWIR spectrometry can detect differences in initial w/c ratios for hardened concretes. Additionally, differences in density and compressive strength as a function of the w/c ratio could be indirectly estimated through SWIR spectrometry.},
	url = {https://www.sciencedirect.com/science/article/pii/S0950061818317598},
	author = {Zohreh Zahiri and Debra Laefer and Aoife Gowen}
}
@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}
}
@inproceedings {1879,
	title = {Preliminary investigation of design parameters of an innovative multi- pinhole system dedicated to brain SPECT imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	month = {11/2018},
	address = {Sydney, Australia},
	author = {Benjamin Auer and Jan De Beenhouwer and Timothy J. Fromme and Kesava Kalluri and Justin C. Goding and George I. Zubal and Lars R. Furenlid and Michael A. King}
}
@article {1903,
	title = {A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila},
	journal = {Plos Computational Biology},
	volume = {14},
	year = {2018},
	pages = {e1006410},
	author = {G Liu and Tanmay Nath and Z Guo and G Linneweber and A Claeys and J Li and M Bengochea and Steve De Backer and Barbara Weyn and M Sneyders and H Nicasy and P Yu and Paul Scheunders and Bassem Hassan}
}
@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 {2003,
	title = {Using Short-wave Infrared Range Spectrometry Data to Determine Brick Characteristics},
	journal = {INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE},
	year = {2018},
	abstract = {Characterizing material strength in-situ for existing structures poses a major problem for a range of civil engineering applications including structural modelling for tunnelling-vulnerability assessment and pre-earthquake resiliency evaluation, especially for unreinforced masonry buildings. Present methods require expensive testing equipment often requiring access to the structure and possible destruction of historic material. This article introduces spectrometry as a non-destructive means for identifying different brick clays and their firing levels, both of which influence the masonry{\textquoteright}s mechanical behavior. The experiments herein considered bricks of 2 clay groups (red and yellow) fired at 3 kiln temperatures (700{\textordmasculine}C, 950{\textordmasculine}C, 1,060{\textordmasculine}C). Samples were examined via spectrometry within the short-wave infrared range (1,300{\textendash}2,200 nm). A Partial Least Square Discriminant Analysis (PLS-DA) model was calibrated using 96 samples and tested on a set of 48 samples, resulting in a 98\% success rate in the classification of the two clay types and a 100\% success rate for classification among the 3 firing levels. The ability of the PLS-DA model to reliably distinguish well-fired bricks from other samples, irrespective of raw material configuration, shows the potential to use this approach as a new, non-destructive means for in-situ assessment of brick for architectural conservation, as well as for safety and serviceability assessments.},
	doi = {10.1080/15583058.2018.1503362},
	url = {https://doi.org/10.1080/15583058.2018.1503362},
	author = {Debra Laefer and Zohreh Zahiri and Aoife Gowen}
}
@article {1824,
	title = {White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults},
	journal = {European Journal of Neuroscience },
	volume = {47},
	year = {2018},
	pages = {446{\textendash}459},
	doi = {10.1111/ejn.13841},
	author = {Hamed Zivari Adab and Sima Chalavi and Iseult Beets and Jolien Gooijers and Inge Leunissen and Boris Cheval and Quinten Collier and Jan Sijbers and Ben Jeurissen and S. P. Swinnen and Matthieu Boisgontier}
}
@article {1712,
	title = {Atom-counting in High Resolution Electron Microscopy: TEM or STEM - that{\textquoteright}s the question},
	journal = {Ultramicroscopy},
	volume = {147},
	year = {2017},
	pages = {112{\textendash}120},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.10.011},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@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}
}
@mastersthesis {1926,
	title = {Optimal statistical experiment design for detecting and locating light atoms using quantitative high resolution (scanning) transmission electron microscopy},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Julie Gonnissen}
}
@article {1804,
	title = {Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {541},
	author = {E.K. Ghasrodashti and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1686,
	title = {Advanced techniques for computational and information sciences},
	journal = {Mathematical Problems in Engineering},
	volume = {2016},
	year = {2016},
	edition = {Article ID 5623913},
	author = {W. Guo and C.-C. Hung and Paul Scheunders and B.-C. Kuo and Y. Lan and Z. Ma}
}
@inproceedings {1809,
	title = {Alternating angle minimization based unmixing with endmember variability},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 6974-6977, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7730819},
	author = {Rob Heylen and Paul Scheunders and Alina Zare and Paul Gader}
}
@article {1688,
	title = {Detecting and locating light atoms from high-resolution STEM images: the quest for a single optimal design Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {170},
	year = {2016},
	pages = {128-138},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.07.014},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@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}
}
@article {1892,
	title = {EPR and DFT analysis of biologically relevant chromium(V) complexes with d-glucitol and d-glucose},
	journal = {Journal of Inorganic Biochemistry},
	volume = {162},
	year = {2016},
	pages = {216 - 226},
	keywords = {1, 2-diolato ligands, DFT, ENDOR, EPR, HYSCORE, Oxidochromium(V)},
	issn = {0162-0134},
	doi = {https://doi.org/10.1016/j.jinorgbio.2016.07.012},
	url = {http://www.sciencedirect.com/science/article/pii/S0162013416302136},
	author = {Sabine Van Doorslaer and Quinten Beirinckx and Kevin Nys and Mar{\'\i}a Florencia Mangiameli and Bert Cuypers and Freddy Callens and Henk Vrielinck and Juan Carlos Gonz{\'a}lez}
}
@article {1685,
	title = {Hyperspectral unmixing with endmember variability via alternating angle minimization},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {4983-4993},
	author = {Rob Heylen and Alina Zare and Paul Gader and Paul Scheunders}
}
@article {1664,
	title = {Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data},
	journal = {Journal of Synchrotron Radiation},
	volume = {23},
	year = {2016},
	pages = {842-849},
	doi = {10.1107/S1600577516005658},
	author = {Daan Pelt and Doga Gursoy and Willem Jan Palenstijn and Jan Sijbers and Francesco De Carlo and Kees Joost Batenburg}
}
@conference {1741,
	title = {Investigating lattice strain in Au nanodecahedrons},
	year = {2016},
	doi = {10.1002/9783527808465.EMC2016.5519},
	author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Jan Sijbers and Van Tendeloo, Gustaaf and Sara Bals}
}
@inproceedings {1810,
	title = {Lidar information extraction by attribute filters with partial reconstruction},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 1484-1487, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7729379},
	author = {Wenzhi Liao and M Della Mura and X Huang and Jocelyn Chanussot and S Gautama and Paul Scheunders and Wilfried Philips}
}
@booklet {1709,
	title = {On the problem of uploading pdf files},
	year = {2016},
	author = {Nikolas Garofil}
}
@proceedings {1906,
	title = {ACIVS 2015, Advanced Concepts for Intelligent Vision Systems},
	volume = {9386},
	year = {2015},
	author = {S Battiato and J Blanc-Talon and G Gallo and Wilfried Philips and D Popescu and Paul Scheunders}
}
@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 {1611,
	title = {Measuring Lattice Strain in Three Dimensions through Electron Microscopy},
	journal = {Nano Letters},
	volume = {15},
	year = {2015},
	pages = {6996{\textendash}7001},
	doi = {10.1021/acs.nanolett.5b03008},
	author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Sara Bals and Jan Sijbers and Van Tendeloo, Gustaaf}
}
@article {1540,
	title = {A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems},
	journal = {Journal of Computational and Applied Mathematics},
	volume = {238},
	year = {2015},
	pages = {1-16},
	abstract = {Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction. },
	doi = {doi:10.1016/j.cam.2014.12.044},
	author = {Siegfried Cools and Pieter Ghysels and Wim Van Aarle and Jan Sijbers and Wim Vanroose}
}
@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}
}
@inproceedings {2042,
	title = {SPIE ProceedingsTowards using eye-tracking data to develop visual-search observers for x-ray breast imaging},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment},
	volume = {9416},
	year = {2015},
	pages = {94160V},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082978},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082978},
	author = {Jiang, Zhengqiang and Zhihua Liang and Das, Mini and Gifford, Howard C.},
	editor = {Mello-Thoms, Claudia R. and Kupinski, Matthew A.}
}
@article {1421,
	title = {The anatomy of the clavicle: A Three-dimensional Cadaveric Study},
	journal = {Clinical anatomy},
	volume = {27},
	year = {2014},
	month = {2013 Oct 21},
	pages = {712{\textendash}723},
	abstract = {The clavicle has a complex osteologic structure that makes morphological analysis extremely difficult. A three-dimensional study was conducted to examine the anatomical variations and characteristics of the bone. Sixty-eight human cadaver clavicles were dissected, CAT-scanned, and reconstructed. An automated parameterization and correspondence shape analysis system was developed. A new length, designated as centerline (CL) length, was defined and measured. This length represents the true length of the clavicle. The endpoint length was measured as the distance between two endpoints. The width and curvature were measured in the axial (AX) and frontal (FR) plane and defined along the CL. Next gender and side characteristics and variations were examined. The mean CL length was 159.0 {\textpm} 11.0 mm. The mean endpoint length was 149.4 {\textpm} 10.3 mm, which was statistically significantly shorter than the CL. The male clavicle was significantly longer (166.8 {\textpm} 7.3 mm vs. 151.0 {\textpm} 8.2 mm), wider (14.6 {\textpm} 1.5 mm vs. 12.7 {\textpm} 1.3 mm lateral FR plane, 25.9 {\textpm} 4.1 mm vs. 23.5 {\textpm} 3.0 mm lateral AX plane and 24.7 {\textpm} 2.8 mm vs. 22.8 {\textpm} 2.8 mm medial AX plane), and more curved (10.8 {\textpm} 2.8 mm vs. 8.6 {\textpm} 2.3 mm medial and 10.5 {\textpm} 3.3 mm vs. 9.1 {\textpm} 2.5 mm lateral) than the female one. Left clavicles were significant longer (159.8 {\textpm} 10.9 mm vs. 158.0 {\textpm} 11.2 mm) than right clavicles. A novel three-dimensional system was developed, used and tested in order to explore the anatomical variations and characteristics of the human clavicle. This information, together with the automated system, can be applied to future clavicle populations and to the design of fixation plates for clavicle fractures. Clin. Anat., 2013. {\textcopyright} 2013 Wiley Periodicals, Inc.},
	issn = {1098-2353},
	doi = {10.1002/ca.22288},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and Gielen, Jan and Van Tongel, Alexander}
}
@inproceedings {1453,
	title = {Automated Social behaviour Recognition At Low Resolution},
	booktitle = {22nd IEEE - International Conference of Pattern Recognition (ICPR)},
	year = {2014},
	author = {Tanmay Nath and G.Liu and Barbara Weyn and Bassem Hassan and Steve De Backer and Paul Scheunders}
}
@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 {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}
}
@article {1409,
	title = {Detecting the adjacency effect in hyperspectral imagery with spectral unmixing techniques},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {1070-1078},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@article {1377,
	title = {Diffusion kurtosis imaging to detect amyloidosis in an APP/PS1 mouse model for Alzheimer{\textquoteright}s disease},
	journal = {Magnetic Resonance in Medicine},
	volume = {69},
	year = {2013},
	pages = {1115{\textendash}1121},
	doi = {10.1002/mrm.24680},
	author = {Greetje Vanhoutte and S. Pereson and Rafael  Delgado Y Palacios and Pieter-Jan Guns and B. Asselbergh and Jelle Veraart and Jan Sijbers and Marleen Verhoye and Van Broeckhoven, Christine and Annemie Van Der Linden}
}
@article {1395,
	title = {Estimation of unknown structure parameters from high-resolution (S)TEM images: what are the limits?},
	journal = {Ultramicroscopy},
	volume = {134},
	year = {2013},
	pages = {34-43},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2013.05.017},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399113001368},
	author = {Arnold Jan den Dekker and Julie Gonnissen and Annick De Backer and Jan Sijbers and Sandra Van Aert}
}
@inproceedings {1523,
	title = {Handling spectral variability with alternating angle minimization},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders and Paul Gader}
}
@article {2039,
	title = {MEKS: A program for computation of inclusive jet cross sections at hadron colliders},
	journal = {Computer Physics Communications},
	volume = {184},
	year = {2013},
	month = {Jan-06-2013},
	pages = {1626 - 1642},
	abstract = {EKS is a numerical program that predicts differential cross sections for production of single-inclusive hadronic jets and jet pairs at next-to-leading order (NLO) accuracy in a perturbative QCD calculation. We describe MEKS 1.0, an upgraded EKS program with increased numerical precision, suitable for comparisons to the latest experimental data from the Large Hadron Collider and Tevatron. The program integrates the regularized patron-level matrix elements over the kinematical phase space for production of two and three partons using the VEGAS algorithm. It stores the generated weighted events in finely binned two-dimensional histograms for fast offline analysis. A user interface allows one to customize computation of inclusive jet observables. Results of a benchmark comparison of the MEKS program and the commonly used FastNLO program are also documented.},
	issn = {00104655},
	doi = {10.1016/j.cpc.2013.01.022},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010465513000611https://api.elsevier.com/content/article/PII:S0010465513000611?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0010465513000611?httpAccept=text/plain},
	author = {Gao, Jun and Liang, Zhihua and Soper, Davison E. and Lai, Hung-Liang and Nadolsky, Pavel M. and Yuan, C.-P.}
}
@inproceedings {1442,
	title = {Memory access optimization for iterative tomography on many-core architectures},
	booktitle = {The 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2013},
	pages = {364-367},
	author = {Wim Van Aarle and Pieter Ghysels and Jan Sijbers and Wim Vanroose}
}
@article {1396,
	title = {Subchronic memantine induced concurrent functional disconnectivity and altered ultra-structural tissue integrity in the rodent brain: revealed by multimodal MRI.},
	journal = {Psychopharmacology},
	volume = {227},
	year = {2013},
	month = {2013 Jun},
	pages = {479-91},
	abstract = {BACKGROUND: An effective NMDA antagonist imaging model may find key utility in advancing schizophrenia drug discovery research. We investigated effects of subchronic treatment with the NMDA antagonist memantine by using behavioural observation and multimodal MRI.

METHODS: Pharmacological MRI (phMRI) was used to map the neuroanatomical binding sites of memantine after acute and subchronic treatment. Resting state fMRI (rs-fMRI) and diffusion MRI were used to study the changes in functional connectivity (FC) and ultra-structural tissue integrity before and after subchronic memantine treatment. Further corroborating behavioural evidences were documented.

RESULTS: Dose-dependent phMRI activation was observed in the prelimbic cortex following acute doses of memantine. Subchronic treatment revealed significant effects in the hippocampus, cingulate, prelimbic and retrosplenial cortices. Decreases in FC amongst the hippocampal and frontal cortical structures (prelimbic, cingulate) were apparent through rs-fMRI investigation, indicating a loss of connectivity. Diffusion kurtosis MRI showed decreases in fractional anisotropy and mean diffusivity changes, suggesting ultra-structural changes in the hippocampus and cingulate cortex. Limited behavioural assessment suggested that memantine induced behavioural effects comparable to other NMDA antagonists as measured by locomotor hyperactivity and that the effects could be reversed by antipsychotic drugs.

CONCLUSION: Our findings substantiate the hypothesis that repeated NMDA receptor blockade with nonspecific, noncompetitive NMDA antagonists may lead to functional and ultra-structural alterations, particularly in the hippocampus and cingulate cortex. These changes may underlie the behavioural effects. Furthermore, the present findings underscore the utility and the translational potential of multimodal MR imaging and acute/subchronic memantine model in the search for novel disease-modifying treatments for schizophrenia.},
	issn = {1432-2072},
	doi = {10.1007/s00213-013-2966-3},
	author = {Sekar, S and Jonckers, E and Marleen Verhoye and Willems, R and Jelle Veraart and Johan Van Audekerke and Couto, J and Giugliano, M and Wuyts, K and Dedeurwaerdere, S and Jan Sijbers and Mackie, C and Ver Donck, L and Steckler, T and Annemie Van Der Linden}
}
@article {CampI.L.N.mverhoyejveraartwvheckeE.S.jsijbersA.avdlinde2011,
	title = {A complementary DTI-histological study in a model of Huntingtons disease},
	journal = {Neurobiology of Aging},
	volume = {33},
	number = {5},
	year = {2012},
	pages = {945-959},
	doi = {http://dx.doi.org/10.1016/j.neurobiolaging.2010.07.001},
	author = {N. Van Camp and Ines Blockx and L. Camon and N. de Vera and Marleen Verhoye and Jelle Veraart and Wim Van Hecke and E. Martinez and Guadelupe S. and Jan Sijbers and A. Planas and Annemie Van Der Linden}
}
@article {1304,
	title = {Diffusion kurtosis imaging in the grading of gliomas},
	journal = {Radiology},
	volume = {2},
	number = {263},
	year = {2012},
	pages = {492-501},
	doi = {10.1148/radiol.12110927},
	author = {Van Cauter, Sofie and Jelle Veraart and Jan Sijbers and Ron R Peeters and U. Himmelreich and S. Van Gool and Van Calenbergh, F. and De Vleeschouwer, S. and Wim Van Hecke and Stefan Sunaert}
}
@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}
}
@inproceedings {1356,
	title = {Imrpoving PLCA-based score-informed source separation with invertible constant-Q transform},
	booktitle = {EUSIPCO 2012, 20th European Signal Processing Conference, Bucharest, Romania, august 27-31 },
	year = {2012},
	pages = {2634-2638},
	author = {Joachim Ganseman and Paul Scheunders and S. Dixon}
}
@inproceedings {1357,
	title = {A jump start for NMF with N-Findr and NNLS},
	booktitle = {DAFx-12, 15-th Int. Conference on Digital Audio Effects, York, UK, september 17-21},
	year = {2012},
	author = {Joachim Ganseman and Paul Scheunders}
}
@inproceedings {1353,
	title = {Unmixing for detection and quantification of adjacency effects},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {3090-3093},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@inbook {1263,
	title = {Classification of Soft Tissue Tumors by Machine Learning Algorithms},
	booktitle = {Soft Tissue Tumors},
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	issn = {978-953-307-862-5},
	author = {Juntu, Jaber and Arthur M. De Schepper and Pieter Van Dyck and Dirk Van Dyck and Jan L Gielen and Paul M Parizel 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}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2010,
	title = {A Decision Support System for the Treatment of Tracheal Stenosis},
	booktitle = {Proc. of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	month = {August},
	pages = {72-76},
	publisher = {IAPR},
	organization = {IAPR},
	address = {Istanbul},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@inproceedings {GansemanMysoreScheundersAbel2010,
	title = {Evaluation of a score-informed source separation system},
	booktitle = {Proc. 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, the Netherlands, August 2010},
	year = {2010},
	pages = {219-224},
	author = {Joachim Ganseman and Gautham J. Mysore and Paul Scheunders and Jonathan S. Abel}
}
@inproceedings {GansemanMysoreScheundersAbel2010,
	title = {Source separation by score synthesis},
	booktitle = {Proc. International Computer Music Conference (ICMC 2010), New York, NY, June 1-5},
	year = {2010},
	pages = {462-465},
	author = {Joachim Ganseman and Gautham J. Mysore and Paul Scheunders and Jonathan S. Abel}
}
@inproceedings {DBLP:conf/interspeech/NamMGLA10,
	title = {A super-resolution spectrogram using coupled PLCA},
	booktitle = {INTERSPEECH},
	year = {2010},
	pages = {1696-1699},
	address = {Makuhari, Japan},
	author = {Juhan Nam and Gautham J. Mysore and Joachim Ganseman and Kyogu Lee and Jonathan S. Abel}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2009,
	title = {Assessment of Tracheal Stenosis Using Active Shape Models of Healthy Tracheas: A Surface Registration Study},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {125-136},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@article {1376,
	title = {A diffusion tensor imaging group study of the spinal cord in multiple sclerosis patients with and without T2 spinal cord lesions.},
	journal = {Journal of magnetic resonance imaging : JMRI},
	volume = {30},
	year = {2009},
	month = {2009 Jul},
	pages = {25-34},
	abstract = {PURPOSE: To examine the T(2)-normal appearing spinal cord of patients with multiple sclerosis (MS) using diffusion tensor imaging.

MATERIALS AND METHODS: Diffusion tensor images of the spinal cord were acquired from 21 healthy subjects, 11 MS patients with spinal cord lesions, and 10 MS patients without spinal cord lesions on the T(2)-weighted MR images. Different diffusion measures were evaluated using both a region of interest (ROI) -based and a diffusion tensor tractography-based segmentation approach.

RESULTS: It was observed that the FA, the transverse diffusivity lambda(perpendicular), and the ratio of the longitudinal and transverse diffusivities (lambda(parallel)/lambda (perpendicular)) were significantly lower in the spinal cord of MS patients with spinal cord lesions compared with the control subjects using both the ROI method (P = 0.014, P = 0.028, and P = 0.039, respectively) and the tractography-based approach (P = 0.006, P = 0.037, and P = 0.012, respectively). For both image analysis methods, the FA and the lambda (parallel)/lambda (perpendicular) values were significantly different between the control group and the MS patient group without T(2) spinal cord lesions (P = 0.013).

CONCLUSION: Our results suggest that the spinal cord may still be affected by MS, even when lesions are not detected on a conventional MR scan. In addition, we demonstrated that diffusion tensor tractography is a robust tool to analyze the spinal cord of MS patients.},
	keywords = {Adult, Analysis of Variance, anisotropy, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Multiple Sclerosis, Observer Variation, Reproducibility of Results, Spinal Cord, Spinal Cord Diseases, Thoracic Vertebrae},
	issn = {1053-1807},
	doi = {10.1002/jmri.21817},
	author = {Wim Van Hecke and Guy Nagels and Emonds, Griet and Alexander Leemans and Jan Sijbers and van Goethem, Johan and Paul M Parizel}
}
@conference {BernatthuysmanGlabbeekjsijbersrpinhoGielen2009,
	title = {Exploring the Clavicle: Morphometric Differences Using a 3D Model},
	year = {2009},
	month = {February},
	address = {Las Vegas,Nevada},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Jan L Gielen}
}
@article {1266,
	title = {Imaging of voids due to deformation in alloy steel using micro-focus X-ray beam},
	journal = {Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms},
	volume = {267},
	year = {2009},
	month = {10/2009},
	pages = {3488 - 3490},
	issn = {0168583X},
	doi = {10.1016/j.nimb.2009.07.005},
	author = {Gupta, C. and Elke Van de Casteele and Chakravartty, J.K.}
}
@article {1614,
	title = {Osteologic exploration of the clavicle: a new approach},
	journal = {The FASEB Journal},
	volume = {23},
	year = {2009},
	edition = { (1_MeetingAbstracts)},
	chapter = {LB9},
	abstract = {Introduction Clavicles have a complex osteologic structure which makes a morphometric analysis extremely difficult. Our analysis shows the exact measurements and variations of the clavicle.

Materials and Methods 90 clavicles were dissected, CAT scanned and reconstructed. All measurements were automatically performed. The length and curvatures were calculated around the central line and for each cross-section the average, sagittal and axial diameter was calculated.

Results The average length is 163{\textpm}11 mm. For the length, there is a 9\% difference between the gender and 1.2\% between the left and right clavicle. Between the genders there is a volume difference of 36\%. The extremities show the biggest diameter, this decrease as approaching to the inflexion point, which is the smallest average diameter. In the axial view the acromial curvature is shorter and more curved than the sternal one. In males, the maximum acromial curvature has a difference of 18\% and the maximum sternal curvature a difference of 4\% compared with the females. In the coronal view there is a concave curvature with a maximum of 6 mm. In females the acromial end bends more posteroinferiorly.

Discussion This is the first 3D analysis performed on the clavicle. Females have a smaller clavicle with a shorter and less curved acromial curvature and a posteroinferior bending. The right clavicle is slightly shorter, thicker and more robust.},
	author = {Hilde Elisa Bortier and Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Gielen, Jan and Guy Hubens}
}
@inproceedings {Gansemanpscheundwdhaes2009,
	title = {Using XML-formatted scores in real-time applications},
	booktitle = {10-th International Society for Music Information Retrieval Conference, pp. 663-668, Kobe, Japan, October 26-30},
	year = {2009},
	pages = {663-668},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@inproceedings {1295,
	title = {Evaluation of conservation treatments for archeological waterlogged wooden artifacts},
	booktitle = {9th International Conference on NDT of Art},
	year = {2008},
	author = {Bugani, Simone and Cloetens, Peter and Colombini, Maria Perla and Giachi, Gianna and Janssens, Koen and Modugno, Francesca and Morselli, Luciano and Elke Van de Casteele}
}
@conference {thuysmanBernatGlabbeekjsijbersGielenBortier2008,
	title = {Left-Right and Gender Analysis of The Human Clavicle, A Problem in Developing an Anatomical Plate?},
	year = {2008},
	month = {September},
	pages = {101},
	address = {Brugge, Belgium},
	author = {Toon Huysmans and Amit Bernat and Francis Van Glabbeek and Jan Sijbers and Jan L Gielen and H. Bortier}
}
@inproceedings {BernatthuysmanGlabbeekjsijbersBortierGielen2008,
	title = {Morphometric Study of the Human Clavicle for the Development of an Anatomical Plate},
	booktitle = {54th Annual Meeting of the Orthopaedic Research Society},
	year = {2008},
	month = {March},
	address = {San Francisco, Ca, United States},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and H. Bortier and Jan L Gielen}
}
@inproceedings {pscheundwdhaes2008,
	title = {Using MusicXML in real-time applications},
	booktitle = {Proceedings of the 34th Euromicro conference on Software Engineering and Advanced Applications, Parma, Italy, September 3-5},
	year = {2008},
	pages = {29-30},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@inproceedings {Gansemanpscheundwdhaes2008,
	title = {Using XQUERY on MusicXML databases for musicological analysis},
	booktitle = {ISMIR 2008, 9-th International Conference on Music Information Retrieval, Philadephia, USA, 14-18 september},
	year = {2008},
	pages = {433-438},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@article {asmolderMartinoStaerenpscheundjsijbersGoebelFormisano2007,
	title = {Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis},
	journal = {Magnetic Resonance Imaging},
	volume = {25},
	number = {6},
	year = {2007},
	month = {July},
	pages = {860-868},
	doi = {doi:10.1016/j.mri.2007.02.018},
	author = {Alain Smolders and F. De Martino and N. Staeren and Paul Scheunders and Jan Sijbers and R. Goebel and E. Formisano}
}
@inproceedings {CampthuysmanmverhoyeGaljartjsijbersavdlinde2007,
	title = {Statistical shape analysis on 3D MRI of the ventricular system of the Cyln2/Rsn double knock-out mice},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {2007},
	pages = {1869},
	address = {Berlin, Germany},
	author = {N. Van Camp and Toon Huysmans and Marleen Verhoye and N. Galjart and Jan Sijbers and Annemie Van Der Linden}
}
@article {1462,
	title = {Voltage Security Enhancement and Congestion Management via STATCOM \& IPFC Using Artificial Intelligence},
	journal = {Iranian Journal of Science and Technology},
	volume = {1},
	number = {12},
	year = {2007},
	chapter = {289},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1468,
	title = {Application of RGA to Optimal Choice and Allocation of UPFC for Voltage Security Enhancement in Deregulated Power System},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@article {1461,
	title = {Coordination of UPFC \& SVC for Voltage Security Enhancement},
	journal = {WSEAS Transactions on Circuits and Systems},
	volume = {5},
	number = {6},
	year = {2006},
	chapter = {740},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1467,
	title = {Optimal Location of STATCOM for Voltage Security Enhancement via Artificial Intelligent},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@proceedings {1469,
	title = {Voltage Security Enhancement by Optimal FACTS Location via RGA},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {jjuntujsijbersdvandyckGielen2005,
	title = {Bias Field Correction for MRI Images},
	booktitle = {Proceedings of the 4th International Conference on Computer Recognition Systems (CORES05)},
	series = {Advances in Computer Science},
	year = {2005},
	month = {May},
	pages = {543-551},
	publisher = {Springer},
	organization = {Springer},
	address = {Rydzyna Castle, Poland},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck and Jan L Gielen},
	editor = {Marek Kurzynsji and Edward Puchala and Michal Wozniak and Andrzej Zolnierek}
}
@article {pjedrasijgarciabdboeckdvandyck1998,
	title = {Optimal filtering versus regularization methods in the Fourier precompensation based proximity neurocorrection in electron beam lithography},
	journal = {Microelectronic Engineering},
	volume = {41},
	year = {1998},
	pages = {195-198},
	author = {P. Jedrasik and J. Garcia and Bart De Boeck and Dirk Van Dyck}
}
@article {dvandyckjgarcia1997,
	title = {INFOTREE: A new generic method for supervised pattern},
	journal = {Pattern Recognition Letters},
	volume = {18},
	year = {1997},
	pages = {1211-1217},
	author = {Dirk Van Dyck and J. Garcia}
}
