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.