Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.

ABSTRACT:

Background

Deep learning (DL)-based adipose tissue segmentation methods have shown great performance and efficacy for adipose tissue distribution analysis using magnetic resonance (MR) images, an important indicator of metabolic health and disease. The aim of this study was to evaluate the reproducibility of whole-body adipose tissue distribution analysis using proton density fat fraction (PDFF) images at different MR strengths.

Methods

A total of 24 volunteers were imaged using both 1.5 and 3.0 T clinical MR imaging (MRI) scanners at two sites. Whole-body PDFF images were acquired covering from neck to knee, and grouped into three subparts: thorax, abdomen, and thigh. The PDFF images were then segmented automatically into subcutaneous adipose tissue (SAT) and internal adipo

SUBMITTER: Cheng C 

PROVIDER: S-EPMC11744155 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature