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Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping.

, , , , , , , , , , , , , , and . MIDL, volume 143 of Proceedings of Machine Learning Research, page 618-632. PMLR, (2021)

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SRflow: Deep learning based super-resolution of 4D-flow MRI data., , , , , , and . Frontiers Artif. Intell., (2022)Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty., , , , , , , , and . UNSURE/PIPPI@MICCAI, volume 12959 of Lecture Notes in Computer Science, page 44-53. Springer, (2021)Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI., , , , and . ISBI, page 1-4. IEEE, (2023)Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting., , , , , , , , , and 6 other author(s). MIDL, volume 121 of Proceedings of Machine Learning Research, page 638-654. PMLR, (2020)Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping., , , , , , , , , and 5 other author(s). MIDL, volume 143 of Proceedings of Machine Learning Research, page 618-632. PMLR, (2021)A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers., , , , and . ISBI, page 1-4. IEEE, (2022)