Cross-Subject Comparison of Local Diffusion MRI Parameters
There has been much interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy...
| المؤلفون الرئيسيون: | , , |
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| التنسيق: | Journal article |
| اللغة: | English |
| منشور في: |
Elsevier
2013
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| _version_ | 1826285174489350144 |
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| author | Smith, S Kindlmann, G Jbabdi, S |
| author_facet | Smith, S Kindlmann, G Jbabdi, S |
| author_sort | Smith, S |
| collection | OXFORD |
| description | There has been much interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images (and other diffusion-derived parameters) in voxelwise statistical analyses, in order to localize brain changes related to development, degeneration, and disease. However, in order to compare such local changes in diffusion parameters across subjects, it is necessary to solve the "correspondence problem," to determine which location in each subject's diffusion images corresponds to the equivalent anatomical location in the other subjects. Some researchers have used generic registration methods to try to achieve correspondence, some have used region-of-interest approaches, some have used tractography to parameterize diffusion parameters according to anatomical location, and some have combined different aspects of all of these approaches to attempt to achieve robust and accurate correspondence. This chapter describes many such approaches in the literature, discusses the potential richness available when using more diffusion-derived information than purely the FA, and also illustrates some of the dangers that the researcher should be aware of when interpreting the analysis of multi-subject diffusion MRI studies. © 2014 Elsevier Inc. All rights reserved. |
| first_indexed | 2024-03-07T01:24:53Z |
| format | Journal article |
| id | oxford-uuid:91a22319-6a3c-4f28-82a5-b2d7d5a0961f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:24:53Z |
| publishDate | 2013 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:91a22319-6a3c-4f28-82a5-b2d7d5a0961f2022-03-26T23:20:00ZCross-Subject Comparison of Local Diffusion MRI ParametersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:91a22319-6a3c-4f28-82a5-b2d7d5a0961fEnglishSymplectic Elements at OxfordElsevier2013Smith, SKindlmann, GJbabdi, SThere has been much interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images (and other diffusion-derived parameters) in voxelwise statistical analyses, in order to localize brain changes related to development, degeneration, and disease. However, in order to compare such local changes in diffusion parameters across subjects, it is necessary to solve the "correspondence problem," to determine which location in each subject's diffusion images corresponds to the equivalent anatomical location in the other subjects. Some researchers have used generic registration methods to try to achieve correspondence, some have used region-of-interest approaches, some have used tractography to parameterize diffusion parameters according to anatomical location, and some have combined different aspects of all of these approaches to attempt to achieve robust and accurate correspondence. This chapter describes many such approaches in the literature, discusses the potential richness available when using more diffusion-derived information than purely the FA, and also illustrates some of the dangers that the researcher should be aware of when interpreting the analysis of multi-subject diffusion MRI studies. © 2014 Elsevier Inc. All rights reserved. |
| spellingShingle | Smith, S Kindlmann, G Jbabdi, S Cross-Subject Comparison of Local Diffusion MRI Parameters |
| title | Cross-Subject Comparison of Local Diffusion MRI Parameters |
| title_full | Cross-Subject Comparison of Local Diffusion MRI Parameters |
| title_fullStr | Cross-Subject Comparison of Local Diffusion MRI Parameters |
| title_full_unstemmed | Cross-Subject Comparison of Local Diffusion MRI Parameters |
| title_short | Cross-Subject Comparison of Local Diffusion MRI Parameters |
| title_sort | cross subject comparison of local diffusion mri parameters |
| work_keys_str_mv | AT smiths crosssubjectcomparisonoflocaldiffusionmriparameters AT kindlmanng crosssubjectcomparisonoflocaldiffusionmriparameters AT jbabdis crosssubjectcomparisonoflocaldiffusionmriparameters |