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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Fang et al. (2024) studied this question.
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