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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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Consistent multi-time-point brain atrophy estimation from the boundary shift integral.

Kelvin K Leung1, Gerard R Ridgway, Sébastien Ourselin

  • 1Dementia Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK. kk.leung@ucl.ac.uk

Neuroimage
|November 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to accurately measure brain atrophy over multiple time-points, crucial for Alzheimer's disease research. The developed technique minimizes bias in brain volume loss calculations from serial MRI scans.

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Neurology

Background:

  • Brain atrophy measurement is vital for neurodegenerative disease studies, especially Alzheimer's disease (AD) drug trials.
  • Automated methods like Boundary Shift Integral (BSI) offer precise atrophy measures but can introduce bias due to asymmetric image registration.
  • Existing symmetric registration methods often limit analysis to two time-points, insufficient for multi-scan longitudinal studies.

Purpose of the Study:

  • To develop a longitudinally consistent, multi-time-point BSI technique for accurate brain atrophy measurement.
  • To address and mitigate systematic bias introduced by asymmetric registration in serial MRI scans.
  • To provide a robust method suitable for therapeutic trials and natural history studies involving multiple scans.

Main Methods:

  • Developed affine registration and differential bias correction using a log-Euclidean average for multi-time-point analysis.
  • Applied the technique to Alzheimer's Disease Neuroimaging Initiative (ADNI) data, including healthy controls, mild cognitive impairment, and AD patients.
  • Assessed bias using four tests: inverse consistency, transitivity, randomly ordered scans, and linear regression of atrophy rates.

Main Results:

  • Traditional BSI with windowed sinc interpolation showed no significant bias.
  • Linear interpolation and asymmetric registration introduced pronounced bias in brain volume loss measurements.
  • Symmetric registration or improved interpolation alone significantly reduced bias.
  • The proposed method, combining improved interpolation and symmetric registration, demonstrated no significant bias across all experiments.

Conclusions:

  • Asymmetric registration and linear interpolation can introduce significant bias in multi-time-point brain atrophy measurements.
  • The novel log-Euclidean based multi-time-point BSI method effectively minimizes bias.
  • This robust technique is suitable for accurate longitudinal brain atrophy assessment in neurodegenerative disease research.