title: Characterization of microcirculation in multiple sclerosis lesions by dynamic texture parameter analysis (DTPA).
abstract: OBJECTIVE
  Texture analysis is an alternative method to quantitatively assess MR-images. In this study, we introduce dynamic texture parameter analysis (DTPA), a novel technique to investigate the temporal evolution of texture parameters using dynamic susceptibility contrast enhanced (DSCE) imaging. Here, we aim to introduce the method and its application on enhancing lesions (EL), non-enhancing lesions (NEL) and normal appearing white matter (NAWM) in multiple sclerosis (MS).
  
  
  METHODS
  We investigated 18 patients with MS and clinical isolated syndrome (CIS), according to the 2010 McDonald's criteria using DSCE imaging at different field strengths (1.5 and 3 Tesla). Tissues of interest (TOIs) were defined within 27 EL, 29 NEL and 37 NAWM areas after normalization and eight histogram-based texture parameter maps (TPMs) were computed. TPMs quantify the heterogeneity of the TOI. For every TOI, the average, variance, skewness, kurtosis and variance-of-the-variance statistical parameters were calculated. These TOI parameters were further analyzed using one-way ANOVA followed by multiple Wilcoxon sum rank testing corrected for multiple comparisons.
  
  
  RESULTS
  Tissue- and time-dependent differences were observed in the dynamics of computed texture parameters. Sixteen parameters discriminated between EL, NEL and NAWM (pAVG = 0.0005). Significant differences in the DTPA texture maps were found during inflow (52 parameters), outflow (40 parameters) and reperfusion (62 parameters). The strongest discriminators among the TPMs were observed in the variance-related parameters, while skewness and kurtosis TPMs were in general less sensitive to detect differences between the tissues.
  
  
  CONCLUSIONS
  DTPA of DSCE image time series revealed characteristic time responses for ELs, NELs and NAWM. This may be further used for a refined quantitative grading of MS lesions during their evolution from acute to chronic state. DTPA discriminates lesions beyond features of enhancement or T2-hypersignal, on a numeric scale allowing for a more subtle grading of MS-lesions.
authors.affiliation: Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland.
authors.forename: Rajeev Kumar
authors.initials: RK
authors.lastname: Verma
authors.forename: Johannes
authors.initials: J
authors.lastname: Slotboom
authors.forename: Mirjam Rachel
authors.initials: MR
authors.lastname: Heldner
authors.forename: Mirjam Rahel
authors.initials: MR
authors.lastname: Heldner
authors.forename: Frauke
authors.initials: F
authors.lastname: Kellner-Weldon
authors.forename: Raimund
authors.initials: R
authors.lastname: Kottke
authors.forename: Christoph
authors.initials: C
authors.lastname: Ozdoba
authors.forename: Christian
authors.initials: C
authors.lastname: Weisstanner
authors.forename: Christian Philipp
authors.initials: CP
authors.lastname: Kamm
authors.forename: Roland
authors.initials: R
authors.lastname: Wiest
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008833:Microcirculation / Q000502:physiology*; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000503:physiopathology*; D055815:Young Adult