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Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of several diseases such as Alzheimer's, Huntington's, Schizophrenia, as well as normal ageing. The presence of deep sulci and 'collapsed gyri' (caused by the loss of tissue in patients with neurodegenerative diseases) complicates the tissue segmentation due to partial volume (PV) effects and limited resolution of MRI. We extend existing work to improve the segmentation and thickness estimation in a single framework. We model the PV effect using a maximum a posteriori approach with novel iterative modification of the prior information to enhance deep sulci and gyri delineation. We use a voxel based approach to estimate thickness using the Laplace equation within a Lagrangian-Eulerian framework leading to sub-voxel accuracy. Experiments performed on a new digital phantom and on clinical Alzheimer's disease MR images show improvements in both accuracy and robustness of the thickness measurements, as well as a reduction of errors in deep sulci and collapsed gyri.

Type

Journal article

Journal

Med Image Comput Comput Assist Interv

Publication Date

2009

Volume

12

Pages

441 - 449

Keywords

Algorithms, Brain, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique