Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study.

ABSTRACT: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases makes early diagnosis important. MRI Proton Density Fat Fraction (PDFF) is the most accurate noninvasive method for quantitatively assessing liver fat but is expensive and has limited availability; accurately quantifying liver fat from more accessible and affordable imaging could potentially improve patient care. This proof-of-concept study explores the feasibility of inferring liver MRI-PDFF values from contrast-enhanced computed tomography (CECT) using deep learning. In this retrospective, cross-sectional study, we analyzed data from living liver

SUBMITTER: Nasir M 

PROVIDER: S-EPMC12333992 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature