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

Authors:
Oscar Acosta;Pierrick Bourgeat;Jurgen Fripp;Erik Bonner;Sébastien Ourselin;Olivier Salvado
Affiliations:
The Australian e-Health Research Centre, ICTC-CSIRO, Brisbane, Australia;The Australian e-Health Research Centre, ICTC-CSIRO, Brisbane, Australia;The Australian e-Health Research Centre, ICTC-CSIRO, Brisbane, Australia;The Australian e-Health Research Centre, ICTC-CSIRO, Brisbane, Australia;Centre for Medical Image Computing, University College London, UK;The Australian e-Health Research Centre, ICTC-CSIRO, Brisbane, Australia
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

Accurate cortical thickness estimation in-vivois 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.