Keywords: Software Tools, Perfusion, Segmentation, DCE-MRI Perfusion, Analysis/Processing
Motivation: In order to extract quantitative measures from perfusion imaging, pharmacokinetic models such as the (extended) Tofts are utilized. These models require contrast agent concentration in plasma to be estimated in a robust way for model fitting.
Goal(s): Develop a method to measure the vascular input function in DCE-MRI brain scans based on superior sagittal sinus (SSS) values which is semi-automatic and robust to user input.
Approach: Given a user selected seed point inside the SSS, image segmentation in combination with voxel-wise intensity analysis is used.
Results: The method was shown to be robust towards user input in a small patient cohort.
Impact: The presented method has the potential to improve the robustness of perfusion parameters, such as Ktrans, ve, and vp, making their usage as quantitative imaging biomarkers more feasible through a more consistent vascular input function definition.
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