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UBC Theses and Dissertations

3D spherical harmonic invariant features for sensitive and robust quantitative shape and function analysis in brain MRI Uthama, Ashish


A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach.

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