BACKGROUND AND PURPOSE: Neuroimaging techniques have the potential to improve acute stroke treatment by selecting the appropriate patients for thrombolytic therapy. In this study, we examined changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) in an animal model of middle cerebral artery occlusion and used these to identify the parameters that best differentiate between oligemic and infarct regions. MATERIALS AND METHODS: Permanent middle cerebral artery occlusion was performed in 17 New Zealand white rabbits. CT perfusion imaging was performed before (baseline), 10, and 30 minutes after the stroke, and then every 30 minutes up to 3 hours. After a final scan at 4 hours, the brain was removed, cut corresponding to CT sections, and stained with 2,3,5-triphenyltetrazolium chloride (TTC) to identify infarcted tissue. A logistic regression model with the 4-hour post-CBF and -CBV values as independent variables was used to determine the binary tissue outcome variable (oligemia or infarction). RESULTS: Infarcted regions were characterized by a significant decrease (P < .005) in both CBV and CBF, whereas oligemic (CBF < 25 mL . 100 g(-1) . min(-1), not infarcted) regions showed a significant decrease (P < .005) in CBF with maintenance of CBV at or near baseline values. From the perfusion parameters at the 4-hour time point, logistic regression by using CBV*CBF resulted in a sensitivity of 90.6% and a specificity of 93.3% for infarction. CONCLUSION: CBF and CBV values obtained from CT perfusion imaging can be used to distinguish between oligemic and infarct regions. This information could be used to assess the viability of ischemic brain tissue.
BACKGROUND AND PURPOSE: Neuroimaging techniques have the potential to improve acute stroke treatment by selecting the appropriate patients for thrombolytic therapy. In this study, we examined changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) in an animal model of middle cerebral artery occlusion and used these to identify the parameters that best differentiate between oligemic and infarct regions. MATERIALS AND METHODS: Permanent middle cerebral artery occlusion was performed in 17 New Zealand white rabbits. CT perfusion imaging was performed before (baseline), 10, and 30 minutes after the stroke, and then every 30 minutes up to 3 hours. After a final scan at 4 hours, the brain was removed, cut corresponding to CT sections, and stained with 2,3,5-triphenyltetrazolium chloride (TTC) to identify infarcted tissue. A logistic regression model with the 4-hour post-CBF and -CBV values as independent variables was used to determine the binary tissue outcome variable (oligemia or infarction). RESULTS:Infarcted regions were characterized by a significant decrease (P < .005) in both CBV and CBF, whereas oligemic (CBF < 25 mL . 100 g(-1) . min(-1), not infarcted) regions showed a significant decrease (P < .005) in CBF with maintenance of CBV at or near baseline values. From the perfusion parameters at the 4-hour time point, logistic regression by using CBV*CBF resulted in a sensitivity of 90.6% and a specificity of 93.3% for infarction. CONCLUSION: CBF and CBV values obtained from CT perfusion imaging can be used to distinguish between oligemic and infarct regions. This information could be used to assess the viability of ischemic brain tissue.
Authors: D G Nabavi; A Cenic; J Dool; R M Smith; F Espinosa; R A Craen; A W Gelb; T Y Lee Journal: J Comput Assist Tomogr Date: 1999 Jul-Aug Impact factor: 1.826
Authors: Max Wintermark; Marc Reichhart; Jean-Philippe Thiran; Philippe Maeder; Marc Chalaron; Pierre Schnyder; Julien Bogousslavsky; Reto Meuli Journal: Ann Neurol Date: 2002-04 Impact factor: 10.422
Authors: L M Hamberg; P Boccalini; G Stranjalis; G J Hunter; Z Huang; E Halpern; R M Weisskoff; M A Moskowitz; B R Rosen Journal: Magn Reson Med Date: 1996-02 Impact factor: 4.668
Authors: M Wintermark; M Reichhart; O Cuisenaire; P Maeder; J-P Thiran; P Schnyder; J Bogousslavsky; R Meuli Journal: Stroke Date: 2002-08 Impact factor: 7.914
Authors: King Chung Ho; William Speier; Haoyue Zhang; Fabien Scalzo; Suzie El-Saden; Corey W Arnold Journal: IEEE Trans Med Imaging Date: 2019-02-25 Impact factor: 10.048
Authors: Stacy L Serber; Brenda Rinsky; Rajesh Kumar; Paul M Macey; Gregg C Fonarow; Ronald M Harper Journal: Nurs Res Date: 2014 May-Jun Impact factor: 2.381
Authors: Santosh K Yadav; Rajesh Kumar; Paul M Macey; Heidi L Richardson; Danny J J Wang; Mary A Woo; Ronald M Harper Journal: Neurosci Lett Date: 2013-09-26 Impact factor: 3.046
Authors: Aliza T Brown; Robert D Skinner; Rene Flores; Leah Hennings; Michael J Borrelli; John Lowery; William C Culp Journal: J Vasc Interv Radiol Date: 2010-04-22 Impact factor: 3.464
Authors: Christopher D d'Esterre; Kenneth M Tichauer; Richard I Aviv; Wolfgang Eisert; Ting-Yim Lee Journal: Transl Stroke Res Date: 2011-01-11 Impact factor: 6.829
Authors: Lesley M Foley; Wendy Fellows-Mayle; T Kevin Hitchens; Joseph E Losee; Timothy Barbano; Michael I Siegel; Mark P Mooney Journal: Childs Nerv Syst Date: 2009-05-05 Impact factor: 1.475