• Cory Lorenz, Thomas Benner, Poe Jou Chen, Chloe Joan Lopez, Hakan Ay, Ming Wang Zhu, Nina M Menezes, Hannu Aronen, Jari Karonen, Yawu Liu, Juho Nuutinen, and A Gregory Sorensen.
• Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Division of Health Sciences and Technology, Harvard/MIT, Charlestown, Massachusetts 08129, USA.
• J Magn Reson Imaging. 2006 Nov 1; 24 (5): 1133-9.

PurposeTo investigate the utility of an automated perfusion-weighted MRI (PWI) method for estimating cerebral blood flow (CBF) based on localized arterial input functions (AIFs) as compared to the standard method of manual global AIF selection, which is prone to deconvolution errors due to the effects of delay and dispersion of the contrast bolus.Materials And MethodsAnalysis was performed on spin- and gradient-echo EPI images from 36 stroke patients. A local AIF algorithm created an AIF for every voxel in the brain by searching out voxels with the lowest delay and dispersion, and then interpolating and spatially smoothing them for continuity. A generalized linear model (GLM) for predicting tissue outcome, and MTT lesion volumes were used to quantify the performance of the localized AIF method in comparison with global methods using ipsilateral and contralateral AIFs.ResultsThe algorithm found local AIFs in each case without error and generated a higher area under the receiver operating characteristic (ROC) curve compared to both global-AIF methods. Similarly, the local MTT lesion volumes had the least mean squared error (MSE).ConclusionAutomated CBF calculation using local AIFs is feasible and appears to produce more useful CBF maps.Copyright (c) 2006 Wiley-Liss, Inc.

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