Abstract #2708
Decomposing cerebral blood flow MRI into functional and structural components
Benjamin Kandel 1,2 , James C. Gee 3 , Jiongjiong Wang 4 , and Brian B Avants 3
1
Bioengineering, University of Pennsylvania,
Philadelphia, PA, United States,
2
Penn
Image Computing and Science Laboratory, Philadelphia,
PA, United States,
3
Penn
Image Computing and Science Laboratory, University of
Pennsylvania, PA, United States,
4
University
of California Los Angeles, CA, United States
Cerebral blood flow (CBF) is partially determined by
brain structure. Current methods for analyzing CBF
imaging techniques, such as arterial spin labeling, only
take into account limited anatomical information. We
propose a method that uses a dictionary learning
approach to provide a more rigorous decomposition of CBF
images into a component that can be predicted by
structural information and a "purely functional"
component that cannot be predicted using brain
structure. This technique has shown to predict a greater
proportion of CBF than segmentation maps, and can be
used for assessing the relative contributions of CBF and
structural imaging.
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