Please use this identifier to cite or link to this item: https://doi.org/10.1038/srep37847
Title: Improving estimation of fiber orientations in diffusion MRI using inter-subject information sharing
Authors: Chen, G
Zhang, P
Li, K
Wee, C.-Y 
Wu, Y
Shen, D
Yap, P.-T
Keywords: brain
diagnostic imaging
diffusion tensor imaging
diffusion weighted imaging
factual database
human
image processing
procedures
signal noise ratio
standard
standards
Brain
Databases, Factual
Diffusion Magnetic Resonance Imaging
Diffusion Tensor Imaging
Humans
Image Processing, Computer-Assisted
Reference Standards
Signal-To-Noise Ratio
Issue Date: 2016
Citation: Chen, G, Zhang, P, Li, K, Wee, C.-Y, Wu, Y, Shen, D, Yap, P.-T (2016). Improving estimation of fiber orientations in diffusion MRI using inter-subject information sharing. Scientific Reports 6 : 37847. ScholarBank@NUS Repository. https://doi.org/10.1038/srep37847
Rights: Attribution 4.0 International
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.
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/178751
ISSN: 20452322
DOI: 10.1038/srep37847
Rights: Attribution 4.0 International
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