MMORF—FSL’s MultiMOdal Registration Framework
We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations withi...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
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MIT Press
2024
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| _version_ | 1797112964569890816 |
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| author | Lange, FJ Arthofer, C Bartsch, A Douaud, G McCarthy, P Smith, SM Andersson, JLR |
| author_facet | Lange, FJ Arthofer, C Bartsch, A Douaud, G McCarthy, P Smith, SM Andersson, JLR |
| author_sort | Lange, FJ |
| collection | OXFORD |
| description | We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL. |
| first_indexed | 2024-04-09T03:56:26Z |
| format | Journal article |
| id | oxford-uuid:e2ba0253-5708-4135-b7f3-5488f7e84c91 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:56:26Z |
| publishDate | 2024 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | oxford-uuid:e2ba0253-5708-4135-b7f3-5488f7e84c912024-03-15T16:07:09ZMMORF—FSL’s MultiMOdal Registration FrameworkJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e2ba0253-5708-4135-b7f3-5488f7e84c91EnglishSymplectic ElementsMIT Press2024Lange, FJArthofer, CBartsch, ADouaud, GMcCarthy, PSmith, SMAndersson, JLRWe present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL. |
| spellingShingle | Lange, FJ Arthofer, C Bartsch, A Douaud, G McCarthy, P Smith, SM Andersson, JLR MMORF—FSL’s MultiMOdal Registration Framework |
| title | MMORF—FSL’s MultiMOdal Registration Framework |
| title_full | MMORF—FSL’s MultiMOdal Registration Framework |
| title_fullStr | MMORF—FSL’s MultiMOdal Registration Framework |
| title_full_unstemmed | MMORF—FSL’s MultiMOdal Registration Framework |
| title_short | MMORF—FSL’s MultiMOdal Registration Framework |
| title_sort | mmorf fsl s multimodal registration framework |
| work_keys_str_mv | AT langefj mmorffslsmultimodalregistrationframework AT arthoferc mmorffslsmultimodalregistrationframework AT bartscha mmorffslsmultimodalregistrationframework AT douaudg mmorffslsmultimodalregistrationframework AT mccarthyp mmorffslsmultimodalregistrationframework AT smithsm mmorffslsmultimodalregistrationframework AT anderssonjlr mmorffslsmultimodalregistrationframework |