title: Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke.
abstract: The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge-preserving spatial regularization for the deconvolution of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke.
  
  
  
  Ischemic tissues are not randomly distributed in the brain but form a spatially organized entity. The addition of a spatial regularization term allows to take into account this spatial organization contrarily to the sole temporal regularization approach which processes each voxel independently. The robustness of the spatial regularization in relation to shape variability, hemodynamic variability in tissues, noise in the magnetic resonance imaging apparatus, and uncertainty on the arterial input function selected for the deconvolution is addressed via an original in silico validation approach.
  
  
  
  The deconvolution algorithm proved robust to the different sources of variability, outperforming temporal Tikhonov regularization in most realistic conditions considered. The limiting factor is the proper estimation of the arterial input function.
  
  
  
  This study quantified the robustness of a spatio-temporal approach for dynamic susceptibility contrast-magnetic resonance imaging deconvolution via a new simulator. This simulator, now accessible online, is of wide applicability for the validation of any deconvolution algorithm. Magn Reson Med 78:1981-1990, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France.
authors.forename: Mathilde
authors.initials: M
authors.lastname: Giacalone
authors.affiliation: University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France.
authors.forename: Carole
authors.initials: C
authors.lastname: Frindel
authors.affiliation: University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France.
authors.forename: Marc
authors.initials: M
authors.lastname: Robini
authors.affiliation: University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France.
authors.forename: Frédéric
authors.initials: F
authors.lastname: Cervenansky
authors.affiliation: ENS-Lyon, UCB Lyon, Inria, NUMED, CNRS, UMPA UMR 5669, LYON, F69007, France.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Grenier
authors.affiliation: University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France.
authors.forename: David
authors.initials: D
authors.lastname: Rousseau
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D002545:Brain Ischemia / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D003287:Contrast Media; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D055420:Perfusion Imaging; D019047:Phantoms, Imaging; D020521:Stroke / Q000000981:diagnostic imaging*
chemical_list: D003287:Contrast Media
keywords: deconvolution; digital phantoms; dynamic susceptibility contrast perfusion MRI; stroke