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Related Experiment Video

Updated: Jun 6, 2026

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

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Adipose segmentation in small animals at 7T: a preliminary study.

Yang Tang1, Susan Lee, Marvin D Nelson

  • 1Department of Radiology, University of Southern California, Childrens Hospital Los Angeles, Los Angeles, USA. ytang@chla.usc.edu

BMC Genomics
|December 15, 2010
PubMed
Summary

This study introduces a novel method for analyzing fat volume in small animals using 7 Tesla MRI. The technique optimizes image acquisition and post-processing for accurate fat segmentation, enhancing adiposity research.

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Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
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Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

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Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
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Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Published on: April 4, 2012

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Small animal MRI at 7 Tesla (T) is crucial for adiposity research.
  • Accurate fat segmentation and analysis are key challenges in this field.
  • Predicting required accuracy and resolution for all studies is difficult due to the evolving nature of research questions.

Purpose of the Study:

  • To develop an optimized method for fat volume analysis in small animal MRI at 7T.
  • To address the need for flexible and reliable fat segmentation techniques.
  • To create a tool that accommodates varying accuracy and spatial resolution requirements.

Main Methods:

  • Utilized a Multi-spin multi-echo (MSME) Bruker pulse sequence, optimized for T1 weighting.
  • Calculated T2 relaxation times pixel by pixel for post-processing.
  • Developed parallel post-processing paths involving image smoothing, segmentation, and a confidence image based on adipose tissue relaxation time distribution.

Main Results:

  • A research tool was created to segment fat, even with low-quality anatomical information.
  • The tool allows for operator adjustments to key steps for comparative analysis.
  • Feasibility was tested on both simulated and real data, demonstrating the method's effectiveness.

Conclusions:

  • The developed method combines T2 parametric information with optimized first echo imaging for enhanced reliability.
  • The innovation lies in pairing specific image acquisition with flexible post-processing.
  • The approach provides more flexible operations and reliable fat separation compared to previous methods.