Artificial intelligence, Diffusion MRI, Algorithm, Computer vision and Iterative reconstruction are his primary areas of study. His Artificial intelligence research includes themes of Noise measurement, Noise and Pattern recognition. His biological study spans a wide range of topics, including Deconvolution, White matter, Voxel and Neuroscience.
Jan Sijbers has included themes like Statistics, Rice distribution and Mathematical optimization in his Algorithm study. His study on Computer vision is mostly dedicated to connecting different topics, such as Reduction. His research in the fields of Discrete tomography, Reconstruction algorithm and Algebraic Reconstruction Technique overlaps with other disciplines such as Dart.
His primary areas of study are Artificial intelligence, Computer vision, Algorithm, Diffusion MRI and Tomography. As a part of the same scientific study, Jan Sijbers usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Noise. Jan Sijbers regularly links together related areas like Imaging phantom in his Computer vision studies.
The Algorithm study combines topics in areas such as Estimator and Magnetic resonance imaging. His Diffusion MRI research integrates issues from White matter and Voxel. His Segmentation study integrates concerns from other disciplines, such as Image processing and Thresholding.
Jan Sijbers focuses on Artificial intelligence, Computer vision, Optics, Pattern recognition and Algorithm. His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence. Jan Sijbers studied Computer vision and Surface that intersect with Basis.
His Optics study combines topics from a wide range of disciplines, such as Monte Carlo method and Iterative reconstruction. In his study, which falls under the umbrella issue of Algorithm, Design of experiments and Series is strongly linked to Estimator. As part of one scientific family, Jan Sijbers deals mainly with the area of Design of experiments, narrowing it down to issues related to the Voxel, and often Diffusion MRI.
His primary areas of investigation include Artificial intelligence, Diffusion MRI, Magnetic resonance imaging, Neuroscience and Pattern recognition. His research integrates issues of Quality, Machine learning and Computer vision in his study of Artificial intelligence. His work on Image resolution as part of general Computer vision study is frequently linked to Set, therefore connecting diverse disciplines of science.
The various areas that he examines in his Diffusion MRI study include Nuclear medicine and Harmonization. His Pattern recognition research is multidisciplinary, incorporating elements of Normalization and Invariant. His Diffusion Kurtosis Imaging study also includes
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ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data
A. Leemans;B. Jeurissen;J. Sijbers;D. K. Jones.
Denoising of diffusion MRI using random matrix theory
Jelle Veraart;Dmitry S. Novikov;Daan Christiaens;Benjamin Ades-aron.
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.
Ben Jeurissen;Alexander Leemans;Jacques Donald Tournier;Derek Kenton Jones.
Human Brain Mapping (2013)
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data
Ben Jeurissen;Jacques-Donald Tournier;Thijs Dhollander;Alan Connelly.
The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography.
Wim van Aarle;Willem Jan Palenstijn;Willem Jan Palenstijn;Jan De Beenhouwer;Thomas Altantzis.
Fast and flexible X-ray tomography using the ASTRA toolbox.
Wim van Aarle;Willem Jan Palenstijn;Jeroen Cant;Eline Janssens.
Optics Express (2016)
Maximum-likelihood estimation of Rician distribution parameters
J. Sijbers;A.J. den Dekker;P. Scheunders;D. Van Dyck.
IEEE Transactions on Medical Imaging (1998)
Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls
Jelle Veraart;Jan Sijbers;Stefan Sunaert;Alexander Leemans.
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution
Ben Jeurissen;Alexander Leemans;Alexander Leemans;Derek Kenton Jones;Jacques-Donald Tournier.
Human Brain Mapping (2011)
Gliomas: Diffusion Kurtosis MR Imaging in Grading
Sofie Van Cauter;Jelle Veraart;Jan Sijbers;Ronald R. Peeters.
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