Abstract
Construction of brain templates 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 a template. 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 intra-voxel inter-subject differences in fiber orientation, fiber configuration, anisotropy, and diffusivity. In this paper, we propose a method to improve the construction of diffusion MRI templates in light of inter-subject differences. Our method involves a novel q-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 q-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 robust to outliers. Our method is therefore in effect seeking the most probable signal profile at each voxel given a distribution of signal profiles. Experimental results show that our method yields diffusion MRI templates with cleaner fiber orientations and less artifacts caused by inter-subject differences in fiber orientation.
Highlights
Brain templates[1,2] capture the common features of a population of images and play crucial roles in the processing and analysis of brain images
In order to simulate the dispersion of fiber orientations across subjects, we generated a set of diffusion signal profiles of fiber bundles oriented according to the Watson probability distribution function[22], which in modified form is given as f (θ|κ) ∝ exp[−κ(1 − cos[2] (θ))], (2)
Our method is less sensitive to outliers and is able to deal with inter-subject fiber dispersion
Summary
Brain templates[1,2] capture the common features of a population of images and play crucial roles in the processing and analysis of brain images. Many methods have been proposed to improve the quality of the constructed template[3,4,5,6,7,8] They sought accurate image alignment to create templates with sharp anatomical details. In this situation, it is unclear for example how signals characterizing fiber bundles of varying orientations, which can occur naturally across subjects, should be fused to form the template. If the distribution of signal profiles of single-directional fiber bundles is contaminated with a small number of signal profiles of crossing fibers, simple averaging will result in a crossing profile, albeit with a small secondary peak This outcome apparently is not representative of the majority. The relevant experimental results, analyses, and discussions are new and not part of our workshop publication
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