Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

ABSTRACT: PURPOSE:This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. PROCEDURES:Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. RESULTS:When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considerin

SUBMITTER: Duan C 

PROVIDER: S-EPMC6374048 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature