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CLADA: Cortical longitudinal atrophy detection algorithm

2010; Elsevier BV; Volume: 54; Issue: 1 Linguagem: Inglês

10.1016/j.neuroimage.2010.07.052

1095-9572

Kunio Nakamura, Robert J. Fox, Elizabeth Fisher,

Cell Image Analysis Techniques

Measurement of changes in brain cortical thickness is useful for the assessment of regional gray matter atrophy in neurodegenerative conditions. A new longitudinal method, called CLADA (cortical longitudinal atrophy detection algorithm), has been developed for the measurement of changes in cortical thickness in magnetic resonance images (MRI) acquired over time. CLADA creates a subject-specific cortical model which is longitudinally deformed to match images from individual time points. The algorithm was designed to work reliably for lower resolution images, such as the MRIs with 1 × 1 × 5 mm3 voxels previously acquired for many clinical trials in multiple sclerosis (MS). CLADA was evaluated to determine reproducibility, accuracy, and sensitivity. Scan–rescan variability was 0.45% for images with 1 mm3 isotropic voxels and 0.77% for images with 1 × 1 × 5 mm3 voxels. The mean absolute accuracy error was 0.43 mm, as determined by comparison of CLADA measurements to cortical thickness measured directly in post-mortem tissue. CLADA's sensitivity for correctly detecting at least 0.1 mm change was 86% in a simulation study. A comparison to FreeSurfer showed good agreement (Pearson correlation = 0.73 for global mean thickness). CLADA was also applied to MRIs acquired over 18 months in secondary progressive MS patients who were imaged at two different resolutions. Cortical thinning was detected in this group in both the lower and higher resolution images. CLADA detected a higher rate of cortical thinning in MS patients compared to healthy controls over 2 years. These results show that CLADA can be used for reliable measurement of cortical atrophy in longitudinal studies, even in lower resolution images.