Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project
Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibr...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/50947/ |
| _version_ | 1848798375248396288 |
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| author | Sotiropoulos, Stamatios N. Hernández-Fernández, Moisés Vu, An T. Andersson, Jesper L. Moeller, Steen Yacoub, Essa Lenglet, Christophe Ugurbil, Kamil Behrens, Timothy E.J. Jbabdi, Saad |
| author_facet | Sotiropoulos, Stamatios N. Hernández-Fernández, Moisés Vu, An T. Andersson, Jesper L. Moeller, Steen Yacoub, Essa Lenglet, Christophe Ugurbil, Kamil Behrens, Timothy E.J. Jbabdi, Saad |
| author_sort | Sotiropoulos, Stamatios N. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually. |
| first_indexed | 2025-11-14T20:18:46Z |
| format | Article |
| id | nottingham-50947 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:18:46Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-509472020-05-04T17:54:13Z https://eprints.nottingham.ac.uk/50947/ Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project Sotiropoulos, Stamatios N. Hernández-Fernández, Moisés Vu, An T. Andersson, Jesper L. Moeller, Steen Yacoub, Essa Lenglet, Christophe Ugurbil, Kamil Behrens, Timothy E.J. Jbabdi, Saad Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually. Elsevier 2016-07-01 Article PeerReviewed Sotiropoulos, Stamatios N., Hernández-Fernández, Moisés, Vu, An T., Andersson, Jesper L., Moeller, Steen, Yacoub, Essa, Lenglet, Christophe, Ugurbil, Kamil, Behrens, Timothy E.J. and Jbabdi, Saad (2016) Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project. NeuroImage, 134 . pp. 396-409. ISSN 1095-9572 https://www.sciencedirect.com/science/article/pii/S1053811916300477 doi:10.1016/j.neuroimage.2016.04.014 doi:10.1016/j.neuroimage.2016.04.014 |
| spellingShingle | Sotiropoulos, Stamatios N. Hernández-Fernández, Moisés Vu, An T. Andersson, Jesper L. Moeller, Steen Yacoub, Essa Lenglet, Christophe Ugurbil, Kamil Behrens, Timothy E.J. Jbabdi, Saad Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project |
| title | Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project |
| title_full | Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project |
| title_fullStr | Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project |
| title_full_unstemmed | Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project |
| title_short | Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project |
| title_sort | fusion in diffusion mri for improved fibre orientation estimation: an application to the 3t and 7t data of the human connectome project |
| url | https://eprints.nottingham.ac.uk/50947/ https://eprints.nottingham.ac.uk/50947/ https://eprints.nottingham.ac.uk/50947/ |