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Related Experiment Video

Updated: May 26, 2026

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging
05:37

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging

Published on: October 20, 2023

Automated liver sampling using a gradient dual-echo Dixon-based technique.

Mustafa R Bashir1, Brian M Dale, Elmar M Merkle

  • 1Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA.

Magnetic Resonance in Medicine
|December 23, 2011
PubMed
Summary
This summary is machine-generated.

An automated liver sampling algorithm improves workflow for magnetic resonance imaging by accurately selecting liver tissue. This technique reduces manual selection, saving time during MRI acquisition.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biophysics

Background:

  • Liver magnetic resonance spectroscopy (MRS) and multiecho chemical shift imaging (CSI) traditionally require manual voxel selection.
  • Manual selection is time-consuming and may not capture a representative portion of the liver parenchyma.
  • A fully automated technique could streamline liver MRI analysis.

Purpose of the Study:

  • To develop and evaluate a fully automated algorithm for liver sampling in MRI.
  • To assess the accuracy, efficiency, and feasibility of the automated algorithm.

Main Methods:

  • Analysis of complete volumes from 3D gradient dual-echo acquisitions (two-point Dixon reconstruction) in 100 subjects at 1.5 T and 3 T.
  • An automated liver sampling algorithm based on ratio pairs of signal intensity data (fat-only/water-only and log(in-phase/opposed-phase)) was applied voxel-by-voxel.
  • Different gridding variations of the algorithm were tested.

Main Results:

  • The automated algorithm achieved average correct liver volume samples ranging from 527 to 733 mL.
  • The percentage of sample located within the liver ranged from 95.4% to 97.1%.
  • Average incorrect extrahepatic volume selected was low (16.5–35.4 mL, 2.9–4.6%), with feasible run times (19.7–79.0 s).

Conclusions:

  • The automated algorithm consistently selects large samples of hepatic parenchyma with minimal extrahepatic contamination.
  • The algorithm's speed and accuracy make it suitable for real-time execution on MRI console during acquisition.
  • This automated approach offers a significant workflow improvement for liver MRI analysis.