RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.
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.
Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, OX3 9DU Headington, U.K. stam@fmrib.ox.ac.uk
Stamatios N
SN
Sotiropoulos
Saad
S
Jbabdi
Jesper L
JL
Andersson
Mark W
MW
Woolrich
Kamil
K
Ugurbil
Timothy E J
TE
Behrens
D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D019047:Phantoms, Imaging; D059629:Signal-To-Noise Ratio