Ajna Borogovac1, Christian Habeck2, Joy Hirsch3, Iris Asllani4
1Biomedical Engineering, Columbia University, New York, NY, United States; 2Neurology, Columbia University; 3Neuroscience & Psychiatry, Columbia University; 4Radiology, Columbia University
Quantification of inter-subject differences in cerebral blood flow (CBF) separately from respective differences in tissue content presents a known challenge in analysis of group data. Recently, our group has developed an algorithm which corrects for partial volume effects (PVE) in arterial spin labeling (ASL) imaging and also yields tissue specific flow density maps (CBFd) which are, theoretically, independent of tissue content. The goals of the present work are to (1) optimize the PVEc algorithm for applications where focal differences in CBFd occur (e.g. in functional imaging) and (2) demonstrate how segmentation can affect accuracy of CBF and CBFd estimation.