Rotation invariant features for HARDI

Authors:
Evan Schwab;H. Ertan Çetingül;Bijan Afsari;Michael A. Yassa;René Vidal
Affiliations:
Center for Imaging Science, Johns Hopkins University;Imaging and Computer Vision, Siemens Corporation, Corporate Technology;Center for Imaging Science, Johns Hopkins University;Department of Psychological and Brain Sciences, Johns Hopkins University;Center for Imaging Science, Johns Hopkins University
Venue:
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Year:
2013

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Abstract

Reducing the amount of information stored in diffusion MRI (dMRI) data to a set of meaningful and representative scalar values is a goal of much interest in medical imaging. Such features can have far reaching applications in segmentation, registration, and statistical characterization of regions of interest in the brain, as in comparing features between control and diseased patients. Currently, however, the number of biologically relevant features in dMRI is very limited. Moreover, existing features discard much of the information inherent in dMRI and embody several theoretical shortcomings. This paper proposes a new family of rotation invariant scalar features for dMRI based on the spherical harmonic (SH) representation of high angular resolution diffusion images (HARDI). These features describe the shape of the orientation distribution function extracted from HARDI data and are applicable to any reconstruction method that represents HARDI signals in terms of an SH basis. We further illustrate their significance in white matter characterization of synthetic, phantom and real HARDI brain datasets.