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Abstract #0436

Retrospective Pharmacokinetic Quantification of Clinical Abdominal DCE-MRI using Deep Learning

Chaowei Wu1,2, Nan Wang3, Srinivas Gaddam4, Hui Han1, Stephen Pandol4, Anthony G. Christodoulou1,2, Yibin Xie1, and Debiao Li1,2
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 3Radiology Department, Stanford University, Stanford, CA, United States, 4Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States


Quantitative dynamic contrast-enhanced (DCE) MRI has the potential for early detection, accurate staging, and therapy monitoring of cancers. However, clinical abdominal DCE-MRI has limited temporal resolution and can only provide qualitative or semi-quantitative assessments of tissue vascularity. In this study, we investigated the feasibility of retrospective quantification of multi-phasic abdominal DCE-MRI by improving the temporal resolution via deep learning. Simulated multi-phasic DCE data was generated using 2-sec temporal-resolution Multitasking DCE images. Results show that DCE kinetic parameters retrospectively estimated by deep learning agree with the ground truth, and are capable of differentiating abnormal tissues.

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