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

Sotiropoulos, S.N. and Jbabdi, S. and Andersson, J.L. and Woolrich, M.W. and Ugurbil, K. and Behrens, T.E.J. (2013) RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. IEEE Transactions on Medical Imaging, 32 (6). pp. 969-982. ISSN 0278-0062

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/714889
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine
Identification Number: https://doi.org/10.1109/TMI.2012.2231873
Depositing User: Eprints, Support
Date Deposited: 11 Jul 2018 12:45
Last Modified: 04 May 2020 16:36
URI: http://eprints.nottingham.ac.uk/id/eprint/52888

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