title: Analysis of perfusion MRI in stroke: To deconvolve, or not to deconvolve.
abstract: There is currently controversy regarding the benefits of deconvolution-based parameters in stroke imaging, with studies suggesting a similar infarct prediction using summary parameters. We investigate here the performance of deconvolution-based parameters and summary parameters for dynamic-susceptibility contrast (DSC) MRI analysis, with particular emphasis on precision.
  
  
  
  Numerical simulations were used to assess the contribution of noise and arterial input function (AIF) variability to measurement precision. A realistic AIF range was defined based on in vivo data from an acute stroke clinical study. The simulated tissue curves were analyzed using two popular singular value decomposition (SVD) based algorithms, as well as using summary parameters.
  
  
  
  SVD-based deconvolution methods were found to considerably reduce the AIF-dependency, but a residual AIF bias remained on the calculated parameters. Summary parameters, in turn, show a lower sensitivity to noise. The residual AIF-dependency for deconvolution methods and the large AIF-sensitivity of summary parameters was greatly reduced when normalizing them relative to normal tissue.
  
  
  
  Consistent with recent studies suggesting high performance of summary parameters in infarct prediction, our results suggest that DSC-MRI analysis using properly normalized summary parameters may have advantages in terms of lower noise and AIF-sensitivity as compared to commonly used deconvolution methods. Magn Reson Med 76:1282-1290, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: Midas
authors.initials: M
authors.lastname: Meijs
authors.affiliation: Stanford Stroke Center, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: Soren
authors.initials: S
authors.lastname: Christensen
authors.affiliation: Stanford Stroke Center, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: Maarten G
authors.initials: MG
authors.lastname: Lansberg
authors.affiliation: Stanford Stroke Center, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: Gregory W
authors.initials: GW
authors.lastname: Albers
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia. fernando.calamante@florey.edu.au.|The Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia. fernando.calamante@florey.edu.au.|Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Australia. fernando.calamante@florey.edu.au.
authors.forename: Fernando
authors.initials: F
authors.lastname: Calamante
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001783:Blood Flow Velocity; D002533:Cerebral Angiography / Q000379:methods*; D002560:Cerebrovascular Circulation; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020521:Stroke / Q000000981:diagnostic imaging* / Q000503:physiopathology*
keywords: arterial input function; deconvolution; dynamic susceptibility contrast MRI; perfusion MRI; stroke