Difference between revisions of "Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets"
| Line 18: | Line 18: | ||
* MGH Algorithms: Peng Yu, P. Ellen Grant, Yuan Qi, Xiao Han, Florent Ségonne, Rudolph Pienaar, Evelina Busa, Jenni Pacheco, Nikos Makris, Randy L. Buckner, Polina Golland, and Bruce Fischl | * MGH Algorithms: Peng Yu, P. Ellen Grant, Yuan Qi, Xiao Han, Florent Ségonne, Rudolph Pienaar, Evelina Busa, Jenni Pacheco, Nikos Makris, Randy L. Buckner, Polina Golland, and Bruce Fischl | ||
| − | [[Category: Shape Analysis]] | + | [[Category: Shape Analysis]] [[Category:MRI]] |
Revision as of 00:59, 8 April 2008
Home < Projects:CorticalSurfaceShapeAnalysisUsingSphericalWaveletsBack to NA-MIC Collaborations, MGH Algorithms
Cortical Surface Shape Analysis Based on Spherical Wavelets
Cortical folding patterns vary both in terms of relative spatial location as well as in spatial frequency content. Wavelets are thus a natural tool for the analysis of folding patterns.
Description
Status Prototype
Submitted IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
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.
Key Investigators
- MGH Algorithms: Peng Yu, P. Ellen Grant, Yuan Qi, Xiao Han, Florent Ségonne, Rudolph Pienaar, Evelina Busa, Jenni Pacheco, Nikos Makris, Randy L. Buckner, Polina Golland, and Bruce Fischl
