Leptomeningeal collateral vessels provide connections between vascular territories in the brain when occlusions occur, e.g., when a person suffers a stroke. The number and connectivity of collateral vessels varies significantly from patient to patient, and they have been shown to play an important role in stroke outcomes, providing blood flow to tissue at risk. Rapid and effective patient-specific determination of the presence and connectivity of collaterals is needed in acute stroke situations to determine which treatments will be effective. We propose to develop an automated system to evaluate collateral vessel trees from computed tomography angiography (CTA) images.
In this github repository (https://github.com/KitwareMedical/ITKTubeTK-StrokeCollateralVessels) we are freely releasing jupyter notebooks for assessing vessel and perfusion characteristics of patients with stroke.
The data processed by these notebooks is restricted from public dissemination per IRB limitations.
If you are authorized to access that data, please do the following:
- Copy Data-Download-Template.sh to Data-Download.sh
- Edit Data-Download.sh, replacing the with your api key from data.kitware.com
These tools and techniques were developed, in part, with funding by NIH/NINDS grant "Automated Assessment of Leptomeningeal Collaterals on CT Angiograms" (R42NS086295, PI: Yueh Lee, MD - Department of Radiology, The University of North Carolina at Chapel Hill) in collaboration with Dr. Marc Niethammer (UNC Computer Science), Christopher Karakasis, MD (Cleveland Clinic Lerner College of Medicine), and Dr. Stephen Aylward (Kitware).
Please use the github issue tracker for bug reports, questions, and feature requests.