Home Computer Science Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016
Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion
Zhanlong Yang, Geng Chen, Dinggang Shen, and Pew-Thian Yap
Abstract Construction of brain atlases is generally carried out using a two-step procedure involving registering a population of images to a common space and then fusing the aligned images to form an atlas. In practice, image registration is not perfect and simple averaging of the images will blur structures and cause artifacts. In diffusion MRI, this is further complicated by the possibility of within- voxel fiber misalignment due to natural inter-subject orientation dispersion. In this paper, we propose a method to improve the construction of diffusion atlases in light of inter-subject fiber dispersion. Our method involves a novel g-space (i.e., wavevector space) patch matching mechanism that is incorporated in a mean shift algorithm to seek the most probable signal at each point in g-space. Our method relies on the fact that the mean shift algorithm is a mode seeking algorithm that converges to the mode of a distribution and is hence robustness to outliers. Our method is therefore in effect seeking the most probable signal profile at each voxel given a distribution of profiles. Experimental results confirm that our method yields cleaner fiber orientation distribution functions with less artifacts caused by dispersion.
Z. Yang and G. Chen contributed equally to this work.
College of Marine, Northwestern Polytechnical University, Xi’an, China Department of Radiology and BRIC, UNC Chapel Hill, Chapel Hill, NC, USA G. Chen
Data Processing Center, Northwestern Polytechnical University, Xi’an, China Department of Radiology and BRIC, UNC Chapel Hill, Chapel Hill, NC, USA D. Shen • P.-T. Yap (H)
© Springer International Publishing AG 2017
A. Fuster et al. (eds.), Computational Diffusion MRI, Mathematics
and Visualization, DOI 10.1007/978-3-319-54130-3_9
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