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.
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.
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||Jul 11, 2018|
|Journal||IEEE Transactions on Medical Imaging|
|Publisher||Institute of Electrical and Electronics Engineers|
|Peer Reviewed||Peer Reviewed|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf|
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf