Abstract #0985

Pharmacokinetic Parameters’ Distribution Estimation with Normalizing Flow in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Ke Fang¹, Zejun Wang², Zhaowei Cheng¹, Bao Wang³, Xinyu Jin¹, Yingchao Liu⁴, and Ruiliang Bai⁵

¹College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China, ²Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ³Department of Radiology, Qilu Hospital of Shandong University, Jinan, China, ⁴Department of Neurosurgery, Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, ⁵School of Medicine, Zhejiang University, Hangzhou, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Analysis

The pharmacokinetic (PK) parameters extracted from the DCE-MRI provide valuable information but suffer from many sources of variability. Thus, the efficient and fast estimation of the distributions of these ambiguous PK parameters caused by variabilities could significantly improve the robustness and repeatability of DCE-MRI. The estimation of the PK parameters’ distributions provides a way to quantify the PK parameters’ values and variabilities simultaneously. In this study, we demonstrated the feasibility of the normalizing flow-based distribution estimation network (FPDEN) for PK parameters’ distribution estimation in DCE-MRI.

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