Cortical Surface Shape Analysis Based on Spherical Wavelets

• Published in: IEEE transactions on medical imaging 2007-04, Vol.26 (4), p.582-597
• Main Authors: Peng Yu, Grant, P.E., Yuan Qi, Xiao Han, Segonne, F., Pienaar, R., Busa, E., Pacheco, J., Makris, N., Buckner, R.L., Golland, P., Fischl, B.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Biomedical imaging; Cerebral Cortex - anatomy & histology; Folding; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; In vivo; Magnetic analysis; Magnetic Resonance Imaging - methods; MRI; multiscale; neurodevelopment; NMR; Nuclear magnetic resonance; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated - methods; Pediatrics; Principal component analysis; Reproducibility of Results; Sensitivity and Specificity; Shape; Studies; Surface reconstruction; Surface waves; Wavelet analysis; Wavelet domain
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2007.892499

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns