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. W...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2013
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| _version_ | 1797062080992378880 |
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| author | Sotiropoulos, SN Jbabdi, S Andersson, J Woolrich, M Ugurbil, K Behrens, T |
| author_facet | Sotiropoulos, SN Jbabdi, S Andersson, J Woolrich, M Ugurbil, K Behrens, T |
| author_sort | Sotiropoulos, SN |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T20:40:26Z |
| format | Journal article |
| id | oxford-uuid:341aebac-f386-46c6-affe-81299f565a09 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:40:26Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:341aebac-f386-46c6-affe-81299f565a092022-03-26T13:24:06ZRubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:341aebac-f386-46c6-affe-81299f565a09EnglishSymplectic Elements at Oxford2013Sotiropoulos, SNJbabdi, SAndersson, JWoolrich, MUgurbil, KBehrens, TThe 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. |
| spellingShingle | Sotiropoulos, SN Jbabdi, S Andersson, J Woolrich, M Ugurbil, K Behrens, T RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. |
| title | RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. |
| title_full | RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. |
| title_fullStr | RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. |
| title_full_unstemmed | RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. |
| title_short | RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. |
| title_sort | rubix combining spatial resolutions for bayesian inference of crossing fibers in diffusion mri |
| work_keys_str_mv | AT sotiropoulossn rubixcombiningspatialresolutionsforbayesianinferenceofcrossingfibersindiffusionmri AT jbabdis rubixcombiningspatialresolutionsforbayesianinferenceofcrossingfibersindiffusionmri AT anderssonj rubixcombiningspatialresolutionsforbayesianinferenceofcrossingfibersindiffusionmri AT woolrichm rubixcombiningspatialresolutionsforbayesianinferenceofcrossingfibersindiffusionmri AT ugurbilk rubixcombiningspatialresolutionsforbayesianinferenceofcrossingfibersindiffusionmri AT behrenst rubixcombiningspatialresolutionsforbayesianinferenceofcrossingfibersindiffusionmri |