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Updated: May 27, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Novel whole brain segmentation and volume estimation using quantitative MRI.

J West1, J B M Warntjes, P Lundberg

  • 1Radiation Physics, Department of Medical and Health Sciences, Faculty of Health Sciences, Linköping University, Linköping, Sweden. janne.west@liu.se

European Radiology
|November 25, 2011
PubMed
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A new quantitative MRI method accurately segments brain tissues like grey matter, white matter, and CSF. This robust approach offers precise volume estimation, crucial for diagnosing neurological conditions.

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Quantitative MRI

Background:

  • Brain tissue segmentation and volume estimation are vital for neurological applications.
  • Volumetric changes in grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are indicative of diseases like MS, Alzheimer's, and normal aging.

Purpose of the Study:

  • To present a novel method for brain tissue segmentation and volume estimation using quantitative magnetic resonance imaging (qMRI).
  • To leverage R(1), R(2), and proton density (PD) parameters for improved tissue characterization.

Main Methods:

  • Utilized previously reported qMRI values for WM, GM, and CSF to define tissues.
  • Employed Bloch simulations to analyze R(1), R(2), and PD in noisy tissue mixtures.
  • Constructed a lookup grid to map R(1)-R(2)-PD space to tissue partial volumes.

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Last Updated: May 27, 2026

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  • Validated the method in 10 healthy subjects across various MRI resolutions and geometries.

    • Main Results:

    • Demonstrated high repeatability for total brain volume (2.2%) with imaging resolution <2.0 mm.
    • Achieved repeatability of 3.2% for WM, 3.2% for GM, and 1.0% for CSF across different resolutions.
    • Showcased repeatability of 2.4% for total brain volume and CSF across different geometries.

    Conclusions:

    • Introduced a novel, robust qMRI-based approach for brain tissue segmentation and volume estimation.
    • The method accounts for partial volume effects by calculating tissue fractions within voxels.
    • Successfully demonstrated the method's utility in a patient with Multiple Sclerosis (MS).