Abstract #0436

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

Chaowei Wu^1,2, Nan Wang³, Srinivas Gaddam⁴, Hui Han¹, Stephen Pandol⁴, Anthony G. Christodoulou^1,2, Yibin Xie¹, and Debiao Li^1,2

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

Synopsis

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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