Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Accelerated Imaging, Tumor Reconstruction, Medical Deep Learning, Multi-contrast Reconstruction
Motivation: Under-sampling is an effective way to reduce MRI acquisition times, but highly under-sampled MRIs lack details required for diagnosis.
Goal(s): We enhance under-sampled MRIs using Deep Learning models that leverage complementary information from another contrast, minimizing differences between enhanced images and their fully-sampled counterparts.
Approach: We train Dense UNets on a dataset containing synthetically under-sampled MRIs of 369 patients with brain tumors and perform quantitative analysis on both the whole brain and tumor tissues.
Results: Models trained with cross-contrast priors outperform those using only under-sampled images and maintain higher fidelity as acceleration increases. Tumor reconstruction errors are higher than in the whole brain.
Impact: Leveraging complementary information from another contrast helps overcome the image fidelity lost at higher acceleration factors, allowing for faster diagnostically useful scanning. We extend the analysis of multi-contrast MRI enhancement to patients with tumors, increasing clinical relevance of results.
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