ArticleQuantitative imaging in medicine and surgery2025
Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Adaptive deep learning for quantification and spatial distribution of abdominal adipose tissue from magnetic resonance imaging proton density fat fraction in adults with a body mass index ≥24 kg/mQuantitative imaging in medicine and surgery · 2026Article
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11 authors.
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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 adipose tissue (IAT) using a U-Net DL model. The volumes of whole body (WH), total adipose tissue (TAT), SAT, and IAT for total body and each subpart were measured, and the volume ratio of TAT/WH, SAT/WH, IAT/WH, SAT/TAT, and IAT/SAT were also calculated. Additionally, the reproducibility of PDFF values of SAT and IAT for total body and subparts were evaluated. Results: The intraclass correlation coefficient (ICC) and Pearson correlation coefficient of these volumes and volume ratios in whole-body between the two scanners were very close to one. The paired Conclusions: Good reproducibility of whole-body adipose tissue distribution analysis using the DL method between 1.5 and 3.0 T MR images was demonstrated, which may facilitate the whole-body adipose tissue distribution analysis using the quantitative MR-PDFF images.
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