Intensity-based 3D motion correction for cardiac MR images
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929298856738816 |
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| author | Stolt-Ansó, Nil Sideri-Lampretsa, Vasiliki Dannecker, Maik Rueckert, Daniel |
| author_facet | Stolt-Ansó, Nil Sideri-Lampretsa, Vasiliki Dannecker, Maik Rueckert, Daniel |
| contents | Cardiac magnetic resonance (CMR) image acquisition requires subjects to hold their breath while 2D cine images are acquired. This process assumes that the heart remains in the same position across all slices. However, differences in breathhold positions or patient motion introduce 3D slice misalignments. In this work, we propose an algorithm that simultaneously aligns all SA and LA slices by maximizing the pair-wise intensity agreement between their intersections. Unlike previous works, our approach is formulated as a subject-specific optimization problem and requires no prior knowledge of the underlying anatomy. We quantitatively demonstrate that the proposed method is robust against a large range of rotations and translations by synthetically misaligning 10 motion-free datasets and aligning them back using the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intensity-based 3D motion correction for cardiac MR images Stolt-Ansó, Nil Sideri-Lampretsa, Vasiliki Dannecker, Maik Rueckert, Daniel Image and Video Processing Computer Vision and Pattern Recognition Cardiac magnetic resonance (CMR) image acquisition requires subjects to hold their breath while 2D cine images are acquired. This process assumes that the heart remains in the same position across all slices. However, differences in breathhold positions or patient motion introduce 3D slice misalignments. In this work, we propose an algorithm that simultaneously aligns all SA and LA slices by maximizing the pair-wise intensity agreement between their intersections. Unlike previous works, our approach is formulated as a subject-specific optimization problem and requires no prior knowledge of the underlying anatomy. We quantitatively demonstrate that the proposed method is robust against a large range of rotations and translations by synthetically misaligning 10 motion-free datasets and aligning them back using the proposed method. |
| title | Intensity-based 3D motion correction for cardiac MR images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.00767 |