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

Researchers

Journal

Modalities

Models

Abstract

Identifying population differences can serve as an insightful tool for diagnostic radiology. To do so, a reliable preprocessing framework and data representation are vital.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.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).This process can be applied to detect population variations in the vasculature automatically.It can guide debugging and inferring complex machine learning algorithms such as SVM and deep learning models.