RubiX: Combining spatial resolutions for bayesian inference of crossing fibers in diffusion MRI

Stamatios N. Sotiropoulos, Saad Jbabdi, Jesper L. Andersson, Mark W. Woolrich, Kamil Ugurbil, Timothy E.J. Behrens

Research output: Contribution to journalArticle

22 Scopus citations

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.

Original languageEnglish (US)
Article number6420959
Pages (from-to)969-982
Number of pages14
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number6
DOIs
StatePublished - Jun 10 2013

Keywords

  • Brain
  • diffusion-weighted imaging
  • inverse methods
  • magnetic resonance imaging (MRI)
  • tractography

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    Sotiropoulos, S. N., Jbabdi, S., Andersson, J. L., Woolrich, M. W., Ugurbil, K., & 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), 969-982. [6420959]. https://doi.org/10.1109/TMI.2012.2231873