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Abstract #0935

Estimation of capillary level input function for abbreviated breast Dynamic Contrast-Enhanced MRI using deep learning approach

Jonghyun Bae1,2,3, Zhengnan Huang1,2,3, Florian Knoll2,3, Krzysztof Geras2,3, Terlika Sood2,3, Laura Heacock2,3, Linda Moy2,3, Li Feng4, and Sungheon Gene Kim5
1NYU School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research, New York, NY, United States, 3Center for Biomedical Imaging, NYU, New York, NY, United States, 4Icahn School of Medicine at Mount Sinai, New York, NY, United States, 5Weill Cornell Medicine, New York, NY, United States

This study proposes a deep learning approach to estimate the capillary level input function(CIF) for contrast kinetic model analysis of dynamic contrast enhanced (DCE)-MRI data. Estimation of the CIF for each voxel eliminates the need for the arterial input function and allows automatic end-to-end analysis. We hypothesize that the CIF serves as a more accurate input function that could yield accurate kinetic parameter estimation, which can be used for the diagnosis between the malignant and the benign cancer in the clinical setting. This hypothesis has been tested with a numerical simulation and breast MRI data from an abbreviated exam.

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