Efficient deformable motion correction for 3-D abdominal MRI using manifold regression
We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial gold-en-angle trajectory. The stack-of-profiles (SoP) from all temporal p...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/46866/ |
| _version_ | 1848797415113490432 |
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| author | Chen, Xin Balfour, Daniel R. Marsden, Paul K. Reader, Andrew J. Prieto, Claudia King, Andrew P. |
| author_facet | Chen, Xin Balfour, Daniel R. Marsden, Paul K. Reader, Andrew J. Prieto, Claudia King, Andrew P. |
| author_sort | Chen, Xin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial gold-en-angle trajectory. The stack-of-profiles (SoP) from all temporal positions are embedded into a common manifold, in which SoPs that were acquired at similar respiratory states are close together. Next, the SoPs in the manifold are clustered into groups using the k-means algorithm. One 3-D volume is reconstructed at the central SoP position of each cluster (a.k.a. key-volumes). Motion fields are estimated using deformable image registration between each of these key-volumes and a reference end-exhale volume. Subsequently, the motion field at any other SoP position in the manifold is derived using manifold regression. The regressed motion fields for each of the SoPs are used to deter-mine a final motion-corrected MRI volume. The method was evaluated on realistic synthetic datasets which were generated from real MRI data and also tested on an in vivo dataset. The framework enables more accurate motion correction compared to the conventional binning-based approach, with high computational efficiency. |
| first_indexed | 2025-11-14T20:03:30Z |
| format | Conference or Workshop Item |
| id | nottingham-46866 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:03:30Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-468662020-05-04T19:04:45Z https://eprints.nottingham.ac.uk/46866/ Efficient deformable motion correction for 3-D abdominal MRI using manifold regression Chen, Xin Balfour, Daniel R. Marsden, Paul K. Reader, Andrew J. Prieto, Claudia King, Andrew P. We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial gold-en-angle trajectory. The stack-of-profiles (SoP) from all temporal positions are embedded into a common manifold, in which SoPs that were acquired at similar respiratory states are close together. Next, the SoPs in the manifold are clustered into groups using the k-means algorithm. One 3-D volume is reconstructed at the central SoP position of each cluster (a.k.a. key-volumes). Motion fields are estimated using deformable image registration between each of these key-volumes and a reference end-exhale volume. Subsequently, the motion field at any other SoP position in the manifold is derived using manifold regression. The regressed motion fields for each of the SoPs are used to deter-mine a final motion-corrected MRI volume. The method was evaluated on realistic synthetic datasets which were generated from real MRI data and also tested on an in vivo dataset. The framework enables more accurate motion correction compared to the conventional binning-based approach, with high computational efficiency. 2017-09-04 Conference or Workshop Item PeerReviewed Chen, Xin, Balfour, Daniel R., Marsden, Paul K., Reader, Andrew J., Prieto, Claudia and King, Andrew P. (2017) Efficient deformable motion correction for 3-D abdominal MRI using manifold regression. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017), 10-14 Sept 2017, Quebec City, Quebec, Canada. 3D abdominal MRI Manifold learning Manifold regression Motion correction https://link.springer.com/chapter/10.1007/978-3-319-66185-8_31 |
| spellingShingle | 3D abdominal MRI Manifold learning Manifold regression Motion correction Chen, Xin Balfour, Daniel R. Marsden, Paul K. Reader, Andrew J. Prieto, Claudia King, Andrew P. Efficient deformable motion correction for 3-D abdominal MRI using manifold regression |
| title | Efficient deformable motion correction for 3-D abdominal MRI using manifold regression |
| title_full | Efficient deformable motion correction for 3-D abdominal MRI using manifold regression |
| title_fullStr | Efficient deformable motion correction for 3-D abdominal MRI using manifold regression |
| title_full_unstemmed | Efficient deformable motion correction for 3-D abdominal MRI using manifold regression |
| title_short | Efficient deformable motion correction for 3-D abdominal MRI using manifold regression |
| title_sort | efficient deformable motion correction for 3-d abdominal mri using manifold regression |
| topic | 3D abdominal MRI Manifold learning Manifold regression Motion correction |
| url | https://eprints.nottingham.ac.uk/46866/ https://eprints.nottingham.ac.uk/46866/ |