title: Stable spline deconvolution for dynamic susceptibility contrast MRI.
abstract: To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI.
  
  
  
  The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets.
  
  
  
  The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD.
  
  
  
  The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Department of Neuroimage, Scientific Institute IRCCS "Eugenio Medea", Bosisio Parini, Italy.
authors.forename: Denis
authors.initials: D
authors.lastname: Peruzzo
authors.affiliation: Department of Information Engineering at the University of Padova, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Castellaro
authors.affiliation: Department of Information Engineering at the University of Padova, Italy.
authors.forename: Gianluigi
authors.initials: G
authors.lastname: Pillonetto
authors.affiliation: Department of Information Engineering at the University of Padova, Italy.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Bertoldo
mesh_terms: D000465:Algorithms; D001783:Blood Flow Velocity; D001921:Brain / Q000098:blood supply* / Q000000981:diagnostic imaging*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003287:Contrast Media / Q000737:chemistry*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis
chemical_list: D003287:Contrast Media
keywords: cerebral blood flow; deconvolution; magnetic resonance imaging; perfusion