Abstract #1287

Estimating the capillary input function using deep learning approach for Dynamic Contrast-Enhanced MRI assessment of blood brain barrier

Jonghyun Bae^1,2, Li Feng³, Krzysztof Geras⁴, Florian Knoll⁴, Yulin Ge^4,5, and Sungheon Gene Kim^4,5

¹Sackler Institute of Graduate Biomedical Science,NYU School of Medicine, New York, NY, United States, ²Radiology, Center for Advanced Imaging Innovation and Research, New York, NY, United States, ³Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁴Center for Advanced Imaging Innovation and Research, New York, NY, United States, ⁵Center for Biomedical Imaging, NYU, New York, NY, United States

This study proposes a deep learning approach of estimating the capillary level of input function for kinetic model analysis on dynamic contrast enhanced (DCE)-MRI data. Our deep-learning network was trained with the numerically synthesized data generated with a wide range of contrast kinetic dynamics with different arterial input function (AIF). We hypothesize that the voxel level capillary input functions would be more accurate input functions for pharmacokinetic analysis. This hypothesis was tested with the DCE-MRI data of healthy subjects.

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