A unified deep learning framework uses parameter-informed contrast disentanglement via ScanCLIP and a severity-aware Mixture-of-Experts network to correct motion artifacts in multi-contrast MRI, reporting PSNR gains of 0.75 dB and better zero-shot generalization on clinical data.
Deep learning- based motion correction in mri: promises, challenges, and future direc- tions.arXiv preprint arXiv:2104.04340, 2021
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Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
A unified deep learning framework uses parameter-informed contrast disentanglement via ScanCLIP and a severity-aware Mixture-of-Experts network to correct motion artifacts in multi-contrast MRI, reporting PSNR gains of 0.75 dB and better zero-shot generalization on clinical data.