GAN and dual-input two-compartment model-based training of a neural network for robust quantification of contrast uptake rate in gadoxetic acid-enhanced MRI.
Journal:
Medical physics
Published Date:
Apr 1, 2020
Abstract
PURPOSE: Gadoxetic acid uptake rate (k ) obtained from dynamic, contrast-enhanced (DCE) magnetic resonance imaging (MRI) is a promising measure of regional liver function. Clinical exams are typically poorly temporally characterized, as seen in a low temporal resolution (LTR) compared to high temporal resolution (HTR) experimental acquisitions. Meanwhile, clinical demands incentivize shortening these exams. This study develops a neural network-based approach to quantitation of k , for increased robustness over current models such as the linearized single-input, two-compartment (LSITC) model.