532 citations
- KTH Royal Institute of TechnologySE3 papers
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- Uppsala UniversitySE2 papers
- Delft University of TechnologyNL1 paper
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- Lund UniversitySE1 paper
- Radboud University Medical CenterNL1 paper
- Radboud University NijmegenNL1 paper
- Skåne University HospitalSE1 paper
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Showing 2017Show all
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stat.AP2017★ 21 cited
Bayesian uncertainty quantification in linear models for diffusion MRI
Jens Sjölund, Anders Eklund, Evren Özarslan +3
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Se…
math.OC2017★ 532 cited
Solving ill-posed inverse problems using iterative deep neural networks
Jonas Adler, Ozan Öktem
We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regu…