Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: Multi-contrast MRI with a single fully-sampled HR image and multiple under-sampled, LR contrasts can streamline diagnostics while reducing scan times.
Goal(s): To effectively reconstruct under-sampled images by extracting anatomical information from the fully-sampled HR reference.
Approach: Employ contrast/anatomy disentanglement learning to preserve anatomical consistency and restore unique contrast features in the LR images.
Results: Preliminary outcomes indicate superior reconstruction fidelity compared to traditional methods, enhancing diagnostic quality and efficiency.
Impact: By preserving imaging quality while reducing MRI scan times, patient convenience is substantially enhanced, allowing scalability across various contrasts.
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