Cortical surface mapping using topology correction, partial flattening and 3D shape context-based non-rigid registration for use in quantifying atrophy in Alzheimer's disease.

Magnetic resonance (MR) provides a non-invasive way to investigate changes in the brain resulting from aging or neurodegenerative disorders such as Alzheimer's disease (AD). Performing accurate analysis for population studies is challenging because of the interindividual anatomical variability. A large set of tools is found to perform studies of brain anatomy and population analysis (FreeSurfer, SPM, FSL). In this paper we present a newly developed surface-based processing pipeline (MILXCTE) that allows accurate vertex-wise statistical comparisons of brain modifications, such as cortical thickness (CTE). The brain is first segmented into the three main tissues: white matter, gray matter and cerebrospinal fluid, after CTE is computed, a topology corrected mesh is generated. Partial inflation and non-rigid registration of cortical surfaces to a common space using shape context are then performed. Each of the steps was firstly validated using MR images from the OASIS database. We then applied the pipeline to a sample of individuals randomly selected from the AIBL study on AD and compared with FreeSurfer. For a population of 50 individuals we found correlation of cortical thickness in all the regions of the brain (average r=0.62 left and r=0.64 right hemispheres). We finally computed changes in atrophy in 32 AD patients and 81 healthy elderly individuals. Significant differences were found in regions known to be affected in AD. We demonstrated the validity of the method for use in clinical studies which provides an alternative to well established techniques to compare different imaging biomarkers for the study of neurodegenerative diseases.

Automatic delineation of sulci and improved partial volume classification for accurate 3D voxel-based cortical thickness estimation from MR.

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 > 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.