title: Cortical surface shape analysis based on spherical wavelets.
abstract: 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.
authors.affiliation: Harvard-MIT Division of Health Sciences and Technology (HST), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139 USA. pengyu@mit.edu
authors.forename: Peng
authors.initials: P
authors.lastname: Yu
authors.forename: P Ellen
authors.initials: PE
authors.lastname: Grant
authors.forename: Yuan
authors.initials: Y
authors.lastname: Qi
authors.forename: Xiao
authors.initials: X
authors.lastname: Han
authors.forename: Florent
authors.initials: F
authors.lastname: Ségonne
authors.forename: Rudolph
authors.initials: R
authors.lastname: Pienaar
authors.forename: Evelina
authors.initials: E
authors.lastname: Busa
authors.forename: Jenni
authors.initials: J
authors.lastname: Pacheco
authors.forename: Nikos
authors.initials: N
authors.lastname: Makris
authors.forename: Randy L
authors.initials: RL
authors.lastname: Buckner
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity