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

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 t-test and Bland-Altman plots for all comparisons showed no significant differences (P>0.05) when comparing the results from the 1.5 T scanner minus those from the 3.0 T scanner. The mean bias for WH, TAT, SAT, and IAT was -6.89 cm3 (P=0.95), -67.21 cm3 (P=0.40), 19.31 cm3 (P=0.74), and -18.84 cm3 (P=0.69), respectively. Good reproducibility performances were also found in each subpart, except for the indices of IAT volume, TAT/WH ratio, and SAT/TAT ratio in the thorax due to different susceptibility effects across MR strengths. The results also demonstrated good reproducibility between PDFF values of the two scanners with the mean bias for WH, thorax, abdomen, and thigh being -0.19% (P=0.219), -0.30% (P=0.118), 0.086% (P=0.494), and 0.24% (P=0.186) for SAT, respectively, as well as 0.35% (P=0.136), 0.46% (P=0.150), 0.58% (P=0.255), and 0.40% (P=0.169) for IAT, respectively.

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.