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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2403.16240 |
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| _version_ | 1866916174774665216 |
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| author | Rendell, Sean Duan, Jinming |
| author_facet | Rendell, Sean Duan, Jinming |
| contents | Diffeomorphic image registration is a commonly used method to deform one image to resemble another. While warping a single image to another is useful, it can be advantageous to warp multiple images simultaneously, such as in tracking the motion of the heart across a sequence of images. In this paper, our objective is to propose a novel method capable of registering a group or sequence of images to a target image, resulting in registered images that appear identical and therefore have a low rank. Moreover, we aim for these registered images to closely resemble the target image. Through experimental evidence, we will demonstrate our method's superior efficacy in producing low-rank groupwise deformations compared to other state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16240 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low Rank Groupwise Deformations for Motion Tracking in Cardiac Cine MRI Rendell, Sean Duan, Jinming Computer Vision and Pattern Recognition Dynamical Systems Optimization and Control Diffeomorphic image registration is a commonly used method to deform one image to resemble another. While warping a single image to another is useful, it can be advantageous to warp multiple images simultaneously, such as in tracking the motion of the heart across a sequence of images. In this paper, our objective is to propose a novel method capable of registering a group or sequence of images to a target image, resulting in registered images that appear identical and therefore have a low rank. Moreover, we aim for these registered images to closely resemble the target image. Through experimental evidence, we will demonstrate our method's superior efficacy in producing low-rank groupwise deformations compared to other state-of-the-art approaches. |
| title | Low Rank Groupwise Deformations for Motion Tracking in Cardiac Cine MRI |
| topic | Computer Vision and Pattern Recognition Dynamical Systems Optimization and Control |
| url | https://arxiv.org/abs/2403.16240 |