Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.

ABSTRACT: Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We co

SUBMITTER: Bliesener Y 

PROVIDER: S-EPMC8887912 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature