title: MRI based diffusion and perfusion predictive model to estimate stroke evolution.
abstract: In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/- 0.19 and 1.20 +/- 0.36. For recovered tissue the mean values were 0.99 +/- 0.25 and 1.87 +/- 0.71, respectively. There was a significant difference between these two regions for both measures (p < 0.003 and p < 0.001, respectively). Mean absolute measures of CBF (ml/100g/min) and CBV (ml/100g) for the total infarcted territory were 33.9 +/- 9.7 and 4.2 +/- 1.9. For recovered MTT tissue, the mean values were 41.5 +/- 7.2 and 5.3 +/- 1.2, respectively. A significant difference was also found for these regions (p < 0.009 and p < 0.036, respectively). The mean measures of sensitivity, specificity, positive and negative predictive values for modeling infarct evolution for the validation patient data were 0.72 +/- 0.05, 0.97 +/- 0.02, 0.68 +/- 0.07 and 0.97 +/- 0.02. We propose that this automated strategy may allow possible guided therapeutic intervention to stroke patients and evaluation of efficacy of novel stroke compounds in clinical drug trials.
authors.affiliation: Centre For Magnetic Resonance, University of Queensland, Brisbane, Queensland 4072, Australia. Stephen.Rose@cmr.uq.edu.au
authors.forename: S E
authors.initials: SE
authors.lastname: Rose
authors.forename: J B
authors.initials: JB
authors.lastname: Chalk
authors.forename: M P
authors.initials: MP
authors.lastname: Griffin
authors.forename: A L
authors.initials: AL
authors.lastname: Janke
authors.forename: F
authors.initials: F
authors.lastname: Chen
authors.forename: G J
authors.initials: GJ
authors.lastname: McLachan
authors.forename: D
authors.initials: D
authors.lastname: Peel
authors.forename: F O
authors.initials: FO
authors.lastname: Zelaya
authors.forename: H S
authors.initials: HS
authors.lastname: Markus
authors.forename: D K
authors.initials: DK
authors.lastname: Jones
authors.forename: A
authors.initials: A
authors.lastname: Simmons
authors.forename: M
authors.initials: M
authors.lastname: O'Sullivan
authors.forename: J M
authors.initials: JM
authors.lastname: Jarosz
authors.forename: W
authors.initials: W
authors.lastname: Strugnell
authors.forename: D M
authors.initials: DM
authors.lastname: Doddrell
authors.forename: J
authors.initials: J
authors.lastname: Semple
mesh_terms: D000368:Aged; D000465:Algorithms; D001783:Blood Flow Velocity; D001810:Blood Volume; D001921:Brain / Q000098:blood supply / Q000503:physiopathology; D002560:Cerebrovascular Circulation; D018450:Disease Progression; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008954:Models, Biological; D011237:Predictive Value of Tests; D020521:Stroke / Q000175:diagnosis / Q000503:physiopathology*