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Related Experiment Video

Updated: Jun 24, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Fast adipose tissue (FAT) assessment by MRI.

S A Gronemeyer1, R G Steen, W M Kauffman

  • 1Department of Diagnostic Imaging, St. Jude's Children's Research Hospital, Memphis, TN, USA. suzanne.gronemeyer@stjude.org

Magnetic Resonance Imaging
|October 12, 2000
PubMed
Summary
This summary is machine-generated.

This study introduces a rapid method for assessing abdominal adipose tissue (FAT) quantity and distribution. An automated analysis of a single navel-level image accurately estimates total, visceral, and subcutaneous fat compared to manual whole-abdomen analysis.

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Area of Science:

  • Medical Imaging
  • Body Composition Analysis
  • Obesity Research

Background:

  • Accurate assessment of adipose tissue (FAT) quantity and distribution is crucial for understanding obesity and related metabolic disorders.
  • Traditional manual methods for quantifying abdominal adipose tissue are time-consuming and labor-intensive.
  • Developing faster, reliable methods for body composition analysis is a significant clinical need.

Purpose of the Study:

  • To develop and validate a rapid, automated method for assessing abdominal adipose tissue (FAT).
  • To compare the accuracy and efficiency of the automated method against manual whole-abdomen analysis.
  • To characterize the quantity and distribution of abdominal adipose tissue in obese females.

Main Methods:

  • Acquisition of 31 contiguous transverse T1-weighted MRI images covering the entire abdomen in 16 obese females.
  • Manual tracing of all adipose tissue volumes by a radiologist for whole-abdomen quantification.
  • Automated analysis of a single image at the umbilical level for rapid adipose tissue measurement.

Main Results:

  • The automated umbilicus-level image method showed significant correlation with manual whole-abdomen analysis (p < 0.001).
  • Excellent agreement was found for subcutaneous adipose tissue (r(2) = 0.958), visceral adipose tissue (r(2) = 0.753), and total adipose tissue (r(2) = 0.941).
  • The automated method required only 6 minutes, compared to 2 hours for the manual method.

Conclusions:

  • Automated analysis of a single umbilical-level MRI image provides a fast and accurate method for abdominal adipose tissue assessment.
  • This technique offers a significant time-saving advantage over traditional manual quantification.
  • The validated automated method can be effectively used for characterizing adipose tissue in obesity research and clinical practice.