ArticleScientific reports2017
Robust Fusion of Diffusion MRI Data for Template Construction.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Construction of a Structurally Unbiased Brain Template with High Image Quality from MRI Scans of Saudi Adult Females.Bioengineering (Basel, Switzerland) · 2025Article
- A framework to construct a longitudinal DW-MRI infant atlas based on mixed effects modeling of dODF coefficients.Computational diffusion MRI. CDMRI (Workshop) · 2020Article
- Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.IEEE transactions on medical imaging · 2019Article
- XQ-SR: Joint x-q space super-resolution with application to infant diffusion MRI.Medical image analysis · 2019Article
- Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space.Medical image analysis · 2019Article
- Article
- Population-averaged atlas of the macroscale human structural connectome and its network topology.NeuroImage · 2018Article
- Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching inFrontiers in neuroinformatics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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