Abstract #0935

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

Jonghyun Bae^1,2,3, Zhengnan Huang^1,2,3, Florian Knoll^2,3, Krzysztof Geras^2,3, Terlika Sood^2,3, Laura Heacock^2,3, Linda Moy^2,3, 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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