Joint modelling of diffusion MRI and microscopy
The combination of diffusion MRI with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate diffusion MRI microstructure models, addressing the indirect nature of dMRI signals. Ty...
| Váldodahkkit: | , , , , , , , |
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| Materiálatiipa: | Journal article |
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Elsevier
2019
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| _version_ | 1826267065823002624 |
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| author | Howard, A Mollink, J Kleinnijenhuis, M Pallebage-Gamarallage, M Bastiani, M Cottaar, M Miller, K Jbabdi, S |
| author_facet | Howard, A Mollink, J Kleinnijenhuis, M Pallebage-Gamarallage, M Bastiani, M Cottaar, M Miller, K Jbabdi, S |
| author_sort | Howard, A |
| collection | OXFORD |
| description | The combination of diffusion MRI with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate diffusion MRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine diffusion MRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a ‘brain-wide’ fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel. |
| first_indexed | 2024-03-06T20:48:30Z |
| format | Journal article |
| id | oxford-uuid:36c2b6c9-0a99-4e0c-8ed7-29be3f46b921 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:48:30Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:36c2b6c9-0a99-4e0c-8ed7-29be3f46b9212022-03-26T13:39:55ZJoint modelling of diffusion MRI and microscopyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:36c2b6c9-0a99-4e0c-8ed7-29be3f46b921Symplectic Elements at OxfordElsevier2019Howard, AMollink, JKleinnijenhuis, MPallebage-Gamarallage, MBastiani, MCottaar, MMiller, KJbabdi, SThe combination of diffusion MRI with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate diffusion MRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine diffusion MRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a ‘brain-wide’ fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel. |
| spellingShingle | Howard, A Mollink, J Kleinnijenhuis, M Pallebage-Gamarallage, M Bastiani, M Cottaar, M Miller, K Jbabdi, S Joint modelling of diffusion MRI and microscopy |
| title | Joint modelling of diffusion MRI and microscopy |
| title_full | Joint modelling of diffusion MRI and microscopy |
| title_fullStr | Joint modelling of diffusion MRI and microscopy |
| title_full_unstemmed | Joint modelling of diffusion MRI and microscopy |
| title_short | Joint modelling of diffusion MRI and microscopy |
| title_sort | joint modelling of diffusion mri and microscopy |
| work_keys_str_mv | AT howarda jointmodellingofdiffusionmriandmicroscopy AT mollinkj jointmodellingofdiffusionmriandmicroscopy AT kleinnijenhuism jointmodellingofdiffusionmriandmicroscopy AT pallebagegamarallagem jointmodellingofdiffusionmriandmicroscopy AT bastianim jointmodellingofdiffusionmriandmicroscopy AT cottaarm jointmodellingofdiffusionmriandmicroscopy AT millerk jointmodellingofdiffusionmriandmicroscopy AT jbabdis jointmodellingofdiffusionmriandmicroscopy |