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The improvements in magnetic resonance imaging have led to the development ofnumerous techniques to better detect structural alterations caused byneurodegenerative diseases. Among these, the patch-based grading framework hasbeen proposed to model local patterns of anatomical changes. This approach isattractive because of its low computational cost and its competitiveperformance. Other studies have proposed to analyze the deformations of brainstructures using tensor-based morphometry, which is a highly interpretableapproach. In this work, we propose to combine the advantages of these twoapproaches by extending the patch-based grading framework with a newtensor-based grading method that enables us to model patterns of localdeformation using a log-Euclidean metric. We evaluate our new method in a studyof the putamen for the classification of patients with pre-manifestHuntington's disease and healthy controls. Our experiments show a substantialincrease in classification accuracy (87.5 $\pm$ 0.5 vs. 81.3 $\pm$ 0.6)compared to the existing patch-based grading methods, and a good complement toputamen volume, which is a primary imaging-based marker for the study ofHuntington's disease.

Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease