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Accurate cortical thickness estimation in-vivo is important for the study of many neurodegenerative diseases. When using magnetic resonance images (MRI), accuracy may be hampered by artifacts such as partial volume (PV) as the cortex spans only a few voxels. In zones of opposed sulcal banks (tight sulci) the measurement can be even more difficult. The aim of this work is to propose a voxel-based cortical thickness estimation method from MR by integrating a mechanism for correcting sulci delineation after an improved partial volume classification. First, an efficient and accurate framework was developed to enhance partial volume classification with structural information. Then, the correction of sulci delineation is performed after a homotopic thinning of a cost function image. Integrated to our voxel-based cortical thickness estimation pipeline, the overall method showed a better estimate of thickness and a high reproducibility on real data (R2 &gt; 0.9). A quantitative analysis on clinical data from an Alzheimer's disease study showed significant differences between normal controls and Alzheimer's disease patients.

Automatic Delineation of Sulci and Improved Partial Volume Classification for Accurate 3D Voxel-Based Cortical Thickness Estimation from MR