We present a robust, multi-stage automated local arterial input function (AIF) method to quantify perfusion using dynamic-susceptibility contrast (DSC)-MRI. We show how this approach reduces potential AIF misclassifications observed in existing automated solutions that can lead to quantification errors and artefacts. Examples of our new approach eliminating such artefacts from scans of subjects who exhibit various cerebrovascular abnormalities are provided, with generated perfusion maps further showing regions of higher cerebral blood flow (CBF) relative to established global AIF methods, consistent with a reduction in quantification errors associated with bolus dispersion.
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