  The improvements in magnetic resonance imaging have led to the development of
numerous techniques to better detect structural alterations caused by
neurodegenerative diseases. Among these, the patch-based grading framework has
been proposed to model local patterns of anatomical changes. This approach is
attractive because of its low computational cost and its competitive
performance. Other studies have proposed to analyze the deformations of brain
structures using tensor-based morphometry, which is a highly interpretable
approach. In this work, we propose to combine the advantages of these two
approaches by extending the patch-based grading framework with a new
tensor-based grading method that enables us to model patterns of local
deformation using a log-Euclidean metric. We evaluate our new method in a study
of the putamen for the classification of patients with pre-manifest
Huntington's disease and healthy controls. Our experiments show a substantial
increase 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 to
putamen volume, which is a primary imaging-based marker for the study of
Huntington's disease.
