title: A 3-D spatio-temporal deconvolution approach for MR perfusion in the brain.
abstract: We propose an original spatio-temporal deconvolution approach for perfusion-weighted MRI applied to cerebral ischemia. The regularization of the underlying inverse problem is achieved with spatio-temporal priors and the resulting optimization problem is solved by half-quadratic minimization. Our approach offers strong convergence guarantees, including when the spatial priors are non-convex. Moreover, experiments on synthetic data and on real data collected from subjects with ischemic stroke show significant performance improvements over the standard approaches-namely, temporal deconvolution based on either truncated singular-value decomposition or ℓ2-regularization-in terms of various performance measures.
authors.affiliation: Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044; INSA-Lyon; Université Lyon 1, France; INSA Lyon, 7 Avenue Jean Capelle, 69621 Villeurbanne, France. Electronic address: carole.frindel@creatis.insa-lyon.fr.
authors.forename: Carole
authors.initials: C
authors.lastname: Frindel
authors.forename: Marc C
authors.initials: MC
authors.lastname: Robini
authors.forename: David
authors.initials: D
authors.lastname: Rousseau
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001783:Blood Flow Velocity; D002545:Brain Ischemia / Q000175:diagnosis* / Q000503:physiopathology*; D002560:Cerebrovascular Circulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis
keywords: Acute stroke; Deconvolution; Perfusion weighted MRI; Spatio-temporal model; Tissue outcome prediction