title: Application of hidden Markov random field approach for quantification of perfusion/diffusion mismatch in acute ischemic stroke.
abstract: The perfusion/diffusion 'mismatch model' in acute ischemic stroke provides the potential to more accurately understand the consequences of thrombolytic therapy on an individual patient basis. Few methods exist to quantify mismatch extent (ischemic penumbra) and none have shown a robust ability to predict infarcted tissue outcome. Hidden Markov random field (HMRF) approaches have been used successfully in many other applications. The aim of the study was to develop a method for rapid and reliable identification and quantification of perfusion/diffusion mismatch using an HMRF approach. An HMRF model was used in combination with automated contralateral identification to segment normal tissue from non-infarcted tissue with perfusion abnormality. The infarct was used as a seed point to initialize segmentation, along with the contralateral mirror tissue. The two seeds were then allowed to compete for ownership of all unclassified tissue. In addition, a novel method was presented for quantifying tissue salvageability by weighting the volume with the degree of hypoperfusion, allowing the penumbra voxels to contribute unequal potential damage estimates. Simulated and in vivo datasets were processed and compared with results from a conventional thresholding approach. Both simulated and in vivo experiments demonstrated a dramatic improvement in accuracy with the proposed technique. For the simulated dataset, the mean absolute error decreased from 171.9% with conventional thresholding to 2.9% for the delay-weighted HMRF approach. For the in vivo dataset, the mean absolute error decreased from 564.6% for thresholding to 34.2% for the delay-weighted HMRF approach. The described method represents a significant improvement over thresholding techniques.
authors.affiliation: Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York, Buffalo, NY, USA. mgdwyer@bnac.net
authors.forename: Michael G
authors.initials: MG
authors.lastname: Dwyer
authors.forename: Niels
authors.initials: N
authors.lastname: Bergsland
authors.forename: Erik
authors.initials: E
authors.lastname: Saluste
authors.forename: Jitendra
authors.initials: J
authors.lastname: Sharma
authors.forename: Zeenat
authors.initials: Z
authors.lastname: Jaisani
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Durfee
authors.forename: Nadir
authors.initials: N
authors.lastname: Abdelrahman
authors.forename: Alireza
authors.initials: A
authors.lastname: Minagar
authors.forename: Romy
authors.initials: R
authors.lastname: Hoque
authors.forename: Frederick E
authors.initials: FE
authors.lastname: Munschauer
authors.forename: Robert
authors.initials: R
authors.lastname: Zivadinov
mesh_terms: D000465:Algorithms; D002545:Brain Ischemia / Q000150:complications / Q000473:pathology*; D002536:Cerebral Arteries / Q000473:pathology / Q000503:physiopathology; D002544:Cerebral Infarction / Q000150:complications / Q000473:pathology*; D002560:Cerebrovascular Circulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008390:Markov Chains; D008875:Middle Aged; D010477:Perfusion / Q000379:methods; D012984:Software; D020521:Stroke / Q000209:etiology / Q000473:pathology*; D013997:Time Factors