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Abstract #4310

Reproducibility of automatic adipose tissue segmentation using PDFF images between 1.5T and 3.0T MR

Chuanli Cheng1, Jingshan Gong2, Hao Peng1, Qian Wan1, Xin Liu1, Hairong Zheng1, and Chao Zou1
1Shenzhen institutes of advanced technology, Chinese Academy of Sciences, Shenzhen, China, 2Shenzhen People's Hospital, Shenzhen, China

Synopsis

Keywords: Data Processing, Fat and Fat/Water Separation, Reproducibility evaluation

Motivation: There is a growing need for standardized and reliable methods for adipose tissue quantification in clinical practice.

Goal(s): To evaluate the reproducibility of the deep-learning based adipose tissue distribution analysis method across different MR filed strengths, and so establish a reproducible and objective method for adipose tissue quantification.

Approach: The whole-body PDFF images of 24 volunteers were acquired at both 1.5 T and 3.0 T scanners. The volumes and PDFF values of the segmented adipose tissue of whole body and subparts were compared.

Results: The results demonstrated good reproducibility of volume and PDFF values between 1.5T and 3.0T scanners with the p-value>0.05.

Impact: These findings could improve adipose tissue assessment in diverse clinical MR settings. This enhancement would enable large cohort studies to better identify obesity-related health risks using multicenter datasets, thus facilitating a more effective approach to obesity management.

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