What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Quantum mechanics
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 most cited work include:
- Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. (613 citations)
- ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data (550 citations)
- Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (494 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (40.56%)
- Computer vision (27.33%)
- Algorithm (18.87%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (40.56%)
- Computer vision (27.33%)
- Optics (10.63%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- The effect of spaceflight and microgravity on the human brain (48 citations)
- Brain Tissue–Volume Changes in Cosmonauts (35 citations)
- TomoBank: a tomographic data repository for computational x-ray science (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
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
- Bayes estimator that connect with fields like Algorithm,
- Kurtosis which connect with Statistical parametric mapping.
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