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AJNR - American Journal of Neuroradiology

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American Journal of Neuroradiology 26:1479-1486, June-July 2005
© 2005 American Society of Neuroradiology

BRAIN

Automated Method for Generating the Arterial Input Function on Perfusion-Weighted MR Imaging: Validation in Patients with Stroke

Michael Mlynash^a, Irina Eyngorn^a, Roland Bammer^c, Michael Moseley^c and David C. Tong^a,b

^a Department of Neurology and Neurological Sciences, Stanford Stroke Center, Palo Alto, CA
^b Departments of Neurology and Neurological Sciences, Stanford University Medical Center, Stanford, CA
^c Department of Radiology, Stanford University Medical Center, Stanford, CA

Address reprint requests to David C. Tong MD, Department of Neurology and Neurological Sciences, Stanford Stroke Center, 701 Welch Road, Suite 325B Palo Alto, CA

BACKGROUND AND PURPOSE: The choice of arterial input function (AIF) can have a profound effect on the blood flow maps generated on perfusion-weighted MR imaging (PWI). Automation of this process could substantially reduce operator dependency, increase consistency, and accelerate PWI analysis. We created an automated AIF identification program (auto-AIF) and validated its performance against conventional manual methods.

METHODS: We compared the auto-AIF against manually derived AIFs from multisection PWIs of 22 patients with stroke. Time to peak, curve width, curve height, and voxel location determined with both techniques were compared. The time to maximum of the tissue residue function (Tmax) and cerebral blood flow (CBF) were computed on a per-pixel basis for each AIF. Spatial patterns of 528 map pairs were compared by computing Pearson correlation coefficients between maps generated with each method.

RESULTS: All auto-AIF–derived PWI map parameters, including bolus peak, width, and height, were consistently superior to manually derived ones. Reproducibility of the auto-AIF–based Tmax maps was excellent (r = 1.0). Paired Tmax maps and CBF maps from both techniques were well correlated (r = 0.82). Time to identify the AIF was significantly shorter with the auto-AIF method than with the manual technique (mean difference, 72 seconds; 95% confidence interval: 54, 89 seconds).

CONCLUSION: An automated program that identifies the AIF is feasible and can create reliably reproducible and accurate Tmax and CBF maps. Automation of this process could reduce PWI analysis time and increase consistency and may allow for more effective use of PWI in the evaluation of acute stroke.

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