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An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillation
[article]
2023
arXiv
pre-print
Although existing deep learning compressed-sensing-based Magnetic Resonance Imaging (CS-MRI) methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since the transition from mathematical analysis to network design not always natural enough, often most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments. In this work, to tackle explainability and generalizability, we propose
arXiv:2205.07062v2
fatcat:lv7uq6jauvho7hrfq4olkfrpfm