title: ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI.
abstract: BACKGROUND
  There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans.
  
  
  OBJECTIVE
  To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images.
  
  
  METHODS
  We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML.
  
  
  RESULTS
  Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively.
  
  
  CONCLUSIONS
  ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.
authors.affiliation: Department of Neuroinflammation, University College London, Institute of Neurology, London, UK. m.yiannakas@ucl.ac.uk
authors.forename: Marios C
authors.initials: MC
authors.lastname: Yiannakas
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Tozer
authors.forename: Klaus
authors.initials: K
authors.lastname: Schmierer
authors.forename: Declan T
authors.initials: DT
authors.lastname: Chard
authors.forename: Valerie M
authors.initials: VM
authors.lastname: Anderson
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Altmann
authors.forename: David H
authors.initials: DH
authors.lastname: Miller
authors.forename: Claudia A M
authors.initials: CA
authors.lastname: Wheeler-Kingshott
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D066127:White Matter / Q000473:pathology*