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Super-resolution in MRI: better images faster?

, , , , , , , , and . Medical Imaging: Image Processing, volume 8314 of SPIE Proceedings, page 83143V. SPIE, (2012)

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Estimating diffusion properties in complex fiber configurations based on structure-adaptive multi-valued tensor-field filtering., , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 941320. SPIE, (2015)General and Efficient Super-Resolution Method for Multi-slice MRI., , and . MICCAI (1), volume 6361 of Lecture Notes in Computer Science, page 615-622. Springer, (2010)Recurrent Inference Machines as inverse problem solvers for MR relaxometry., , , , , , , and . CoRR, (2021)Non-rigid Groupwise Image Registration for Motion Compensation in Quantitative MRI., , , , , , , , , and 2 other author(s). WBIR, volume 8545 of Lecture Notes in Computer Science, page 184-193. Springer, (2014)Total Correlation-Based Groupwise Image Registration for Quantitative MRI., , , , , , , , and . CVPR Workshops, page 626-633. IEEE Computer Society, (2016)Optimal Experimental Design for Diffusion Kurtosis Imaging., , , , and . IEEE Trans. Medical Imaging, 29 (3): 819-829 (2010)Joint Maximum Likelihood Estimation of Motion and T1 Parameters from Magnetic Resonance Images in a Super-resolution Framework: a Simulation Study., , , , , , , and . Fundam. Informaticae, 172 (2): 105-128 (2020)A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine., , , , , , , , and . NeuroImage, (2022)Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model., , , , , , and . ISBI, page 783-786. IEEE, (2017)High resolution T1 estimation from multiple low resolution magnetic resonance images., , , , and . ISBI, page 1036-1039. IEEE, (2015)