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Abstract #3221

Cross-Contrast Enhancement of Brain Tumor Datasets using Deep Learning

Robert Richard Griffin1, Michael Murphy1, Hosein Neeli1, Ernst Leiss1, Andrew Webb2, Diego Martin3, Nikolaos Tsekos1, and Phillip Martin3
1Computer Science, University of Houston, Houston, TX, United States, 2C.J. Gorter MRI Center, Leiden University, Leiden, Netherlands, 3Department of Radiology, Houston Methodist Research Institute, Houston, TX, United States

Synopsis

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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Keywords