Robust Fusion of Diffusion MRI Data for Template Construction. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Yang, Zhanlong; Chen, Geng; Shen, Dinggang; Yap, Pew-Thian
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 16
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-13247-w
• Languages: English
• ISSNs: 2045-2322

Abstract

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 novelq -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 inq -space. Our method relies on the fact that the mean shift algorithm is amode 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.