Determining and Validating Population Differences in Magnetic Resonance Angiography Using Sparse Representation

Goal: Identifying population differences, in vascular anatomy can serve as an insightful tool for diagnostic radiology. To do so, a reliable preprocessing framework and data representation are vital. Methods: We build a machine learning model to visualize gender differences in the circle of Willis (CoW), an integral part of the brain’s vasculature. We process a dataset of 570 individuals and process them for analysis using 389 for the final analysis. Results: Statistical differences were identified between male and female patients in one image plane and visualize where they are. We can see differences between the right and left-hand sides of the brain confirmed using Support Vector Machines (SVM), at p=0.006. Conclusion: This process can be applied to detect population variations in the vasculature automatically. Significance: It can guide debugging and inferring complex machine learning algorithms such as SVM and deep learning models.