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RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI

Sotiropoulos, S.N.; Jbabdi, S.; Andersson, J.L.; Woolrich, M.W.; Ugurbil, K.; Behrens, T.E.J.

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Authors

S.N. Sotiropoulos

S. Jbabdi

J.L. Andersson

M.W. Woolrich

K. Ugurbil

T.E.J. Behrens



Abstract

The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.

Citation

Sotiropoulos, S., Jbabdi, S., Andersson, J., Woolrich, M., Ugurbil, K., & Behrens, T. (2013). RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. IEEE Transactions on Medical Imaging, 32(6), https://doi.org/10.1109/TMI.2012.2231873

Journal Article Type Article
Acceptance Date Nov 28, 2012
Online Publication Date Jan 25, 2013
Publication Date May 29, 2013
Deposit Date Jul 11, 2018
Publicly Available Date Mar 28, 2024
Journal IEEE Transactions on Medical Imaging
Print ISSN 0278-0062
Electronic ISSN 0278-0062
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 32
Issue 6
DOI https://doi.org/10.1109/TMI.2012.2231873
Public URL https://nottingham-repository.worktribe.com/output/714889
Publisher URL https://ieeexplore.ieee.org/document/6420959/

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