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Abstract #2122

RubiX: Combining Spatial Resolutions for Bayesian Inference of Crossing Fibres in Diffusion MRI

Stamatios N. Sotiropoulos1, Saad Jbabdi1, Jesper L. Andersson1, Mark W. Woolrich1, 2, Kamil Ugurbil3, Timothy E.J. Behrens1

1FMRIB Centre, University of Oxford, Oxford, United Kingdom; 2Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; 3Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

The trade-off between signal to noise ratio and spatial specificity governs the choice of spatial resolution in diffusion-weighted magnetic resonance imaging. We present an approach for tackling this trade-off by combining data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fibre patterns, therefore, combining the benefits of each acquisition. We show that fibre crossings at the highest spatial resolution can be inferred more robustly using this model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.