Abstract
Extracting population-wise information from medical images, specifically in the neurological domain, is crucial to better understanding disease processes and progression. This is frequently done in a whole-brain voxel-wise manner, in which a population of patients and healthy controls are registered to a common co-ordinate space and a statistical test is performed on the distribution of image intensities for each location. Although this method has yielded a number of scientific insights, it is further from clinical applicability as the differences are often small and altogether do not permit for a high-performing classifier. In this article, we take the opposite approach of using a high-performing classifier, specifically a traditional convolutional neural network, and then extracting insights from it which can be applied in a population-wise manner, a method we call voxel-based diktiometry. We have applied this method to diffusion tensor imaging (DTI) analysis for Parkinson's disease (PD), using the Parkinson's Progression Markers Initiative database. By using the network sensitivity information, we can decompose what elements of the DTI contribute the most to the network's performance, drawing conclusions about diffusion biomarkers for PD that are based on metrics which are not readily expressed in the voxel-wise approach.