@article {2256, title = {ADEPT: Accurate Diffusion EPI with multi-contrast shoTs}, journal = {Magnetic Resonance in Medicine}, volume = {89}, year = {2023}, pages = {396-410}, doi = {10.1002/mrm.29398}, author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Stefan Klein and Johan Van Audekerke and Verhoye, Marleen and Arnold Jan den Dekker and Jan Sijbers} } @article {2297, title = {dtiRIM: A generalisable deep learning method for diffusion tensor imaging}, journal = {Neuroimage}, volume = {269}, year = {2023}, chapter = {119900}, doi = {10.1016/j.neuroimage.2023.119900}, author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Ben Jeurissen and Dirk H J Poot} } @article {2289, title = {Systematic review of reconstruction techniques for accelerated quantitative MRI}, journal = {Magnetic Resonance in Medicine}, volume = {90}, year = {2023}, pages = {1172-1208}, doi = {https://doi.org/10.1002/mrm.29721}, author = {Banafshe Shafieizargar and Riwaj Byanju and Jan Sijbers and Stefan Klein and Arnold Jan den Dekker and Dirk H J Poot} } @article {2190, title = {Recurrent Inference Machines as inverse problem solvers for MR relaxometry}, journal = {Medical Image Analysis}, volume = {74}, year = {2021}, pages = {1-11}, doi = {https://doi.org/10.1016/j.media.2021.102220}, author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot} } @conference {2019, title = {Optimal design of a T1 super-resolution reconstruction experiment: a simulation study}, year = {2020}, author = {Michele Nicastro and Quinten Beirinckx and Piet Bladt and Ben Jeurissen and Stefan Klein and Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker} } @conference {1984, title = {A deep learning approach to T1 mapping in quantitative MRI}, volume = {32 (Suppl. 1)}, number = {S09.05}, year = {2019}, publisher = {Magn Reson Mater Phy}, abstract = {Quantitative MRI aims to measure biophysical tissue parameters through the analysis of the MR signal. Conventional parameter estimation methods, which often rely on a voxel-wise mapping, ignores spatial redundancies. In this work, a deep learning method for T1 mapping is proposed to overcome this limitation.}, doi = {10.1007/s10334-019-00754-2}, author = {Ribeiro Sabidussi, Emanoel and Michele Nicastro and Shabab Bazrafkan and Quinten Beirinckx and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker and Stefan Klein and Dirk H J Poot} }