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

Abstract

Goal: Identifying population differences 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 start with a dataset of 570 individuals and process them for analysis using 389 for the final analysis.

Results: We find statistical differences 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).

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.