ArticleScientific reports2016
Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Validating pore size estimates in a complex microfiber environment on a human MRI system.Magnetic resonance in medicine · 2021Article
- Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.IEEE transactions on medical imaging · 2019Article
- 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
- Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching inFrontiers in neuroinformatics · 2018Article
- Robust Fusion of Diffusion MRI Data for Template Construction.Scientific reports · 2017Article
- Fiber Orientation Estimation Guided by a Deep Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
- Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
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Authors and funding
7 authors.
Funding
Abstract
Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.