ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated

Marios C Yiannakas1, Daniel J Tozer, Klaus Schmierer

  • 1Department of Neuroinflammation, University College London, Institute of Neurology, London, UK. m.yiannakas@ucl.ac.uk

Multiple Sclerosis (Houndmills, Basingstoke, England)
|October 6, 2012
PubMed
Abstract

Insights

ADvanced IMage Algebra (ADIMA) reveals white matter lesion (WML) heterogeneity in multiple sclerosis (MS) using MRI. This novel method reproducibly classifies WMLs into distinct subgroups based on quantitative magnetic resonance properties.

Area of Science:

  • Neuroimaging
  • Radiology
  • Biomedical Engineering

Background:

  • Correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes on T2-weighted (T2w) MRI are modest.
  • This limitation may stem from the inability of T2w scans to reveal pathological heterogeneity within WMLs.

Purpose of the Study:

  • To evaluate ADvanced IMage Algebra (ADIMA), a novel MRI post-processing technique.
  • To determine if ADIMA can identify WML heterogeneity using proton-density weighted (PDw) and T2w images.

Main Methods:

  • Conventional PDw and T2w MRI scans from 10 relapsing-remitting MS patients were analyzed.
  • ADIMA was used to segment WMLs into bright (ADIMA-b) and dark (ADIMA-d) sub-regions.
  • Reproducibility of segmentation for ADIMA-b, ADIMA-d, and T2-WML was assessed.

Main Results:

  • ADIMA-derived volumes showed significant correlation with conventional lesion volumes (p < 0.05).
  • ADIMA-b demonstrated distinct quantitative magnetic resonance properties (higher T1/T2, lower MTR) compared to T2-WML (p < 0.001).
  • ADIMA-d exhibited similar quantitative characteristics to T2-WML, with partial overlap observed between regions.

Conclusions:

  • ADIMA effectively classifies WMLs into two distinct subgroups based on quantitative magnetic resonance properties.
  • These subgroups possess different pathological characteristics, offering insights into WML heterogeneity.
  • The ADIMA method provides reproducible classification of WMLs in MS patients.

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