Texture Analysis to Detect Cerebral Degeneration in Amyotrophic Lateral Sclerosis

Background: Evidence of cerebral degeneration is not apparent on routine brain MRI in amyotrophic lateral sclerosis (ALS). Texture analysis can detect change in images based on the statistical properties of voxel intensities. Our objective was to test the utility of texture analysis in detecting cerebral degeneration in ALS. A secondary objective was to determine whether the performance of texture analysis is dependent on image resolution. Methods: High-resolution (0.5 x 0.5 mm(2) in-plane) coronal T2-weighted MRI of the brain were acquired from 12 patients with ALS and 19 healthy controls on a 4.7 Tesla MRI system. Image data sets at lower resolutions were created by down-sampling to 1 x 1, 2 x 2, 3 x 3, and 4 x 4 mm(2). Texture features were extracted from a slice encompassing the corticospinal tract at the different resolutions and tested for their discriminatory power and correlations with clinical measures. Subjects were also classified by visual assessment by expert reviewers. Results: Texture features were different between ALS patients and healthy controls at 1 x 1, 2 x 2, and 3 x 3 mm(2) resolutions. Texture features correlated with measures of upper motor neuron function and disability. Optimal classification performance was achieved when best-performing texture features were combined with visual assessment at 2 x 2 mm(2) resolution (0.851 area under the curve, 83% sensitivity, 79% specificity). Conclusions: Texture analysis can detect subtle abnormalities in MRI of ALS patients. The clinical yield of the method is dependent on image resolution. Texture analysis holds promise as a potential source of neuroimaging biomarkers in ALS.