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) faces 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 pattern...
| मुख्य लेखकों: | , , , , , , , , , |
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| स्वरूप: | Journal article |
| प्रकाशित: |
Elsevier
2016
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| _version_ | 1826273395450314752 |
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| author | Sotiropoulos, S Hernandez-Fernandez, M Vu, A Andersson, J Moeller, S Yacoub, E Lenglet, C Ugurbil, K Behrens, T Jbabdi, S |
| author_facet | Sotiropoulos, S Hernandez-Fernandez, M Vu, A Andersson, J Moeller, S Yacoub, E Lenglet, C Ugurbil, K Behrens, T Jbabdi, S |
| author_sort | Sotiropoulos, S |
| collection | OXFORD |
| description | Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) faces 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 | 2024-03-06T22:27:32Z |
| format | Journal article |
| id | oxford-uuid:57288a30-bd84-4c39-a0b7-f7e7dbed7da2 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:27:32Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:57288a30-bd84-4c39-a0b7-f7e7dbed7da22022-03-26T16:54:55ZFusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome ProjectJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:57288a30-bd84-4c39-a0b7-f7e7dbed7da2Symplectic Elements at OxfordElsevier2016Sotiropoulos, SHernandez-Fernandez, MVu, AAndersson, JMoeller, SYacoub, ELenglet, CUgurbil, KBehrens, TJbabdi, SDetermining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) faces 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. |
| spellingShingle | Sotiropoulos, S Hernandez-Fernandez, M Vu, A Andersson, J Moeller, S Yacoub, E Lenglet, C Ugurbil, K Behrens, T Jbabdi, S 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 |
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