Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data
Kang Wang, Guilherme Moura Cunha, Kyle Hasenstab, Walter C. Henderson, Michael S. Middleton, Shelley A. Cole, Jason G. Umans, Tauqeer Ali, Albert Hsiao, Claude B. Sirlin
Abstract
BACKGROUND. The confounder-corrected chemical shift–encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted in- and opposed-phase (IOP) images, although signal FF is prone to biases, leading to inaccurate quantification.
Topics & Concepts
MedicineMagnetic resonance imagingPopulationVoxelNuclear medicineArtificial intelligenceRadiologyComputer scienceEnvironmental healthLiver Disease Diagnosis and TreatmentAdvanced MRI Techniques and ApplicationsMRI in cancer diagnosis