On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929363829653504 |
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| author | Thies, Mareike Wagner, Fabian Maul, Noah Mei, Siyuan Gu, Mingxuan Pfaff, Laura Vysotskaya, Nastassia Yu, Haijun Maier, Andreas |
| author_facet | Thies, Mareike Wagner, Fabian Maul, Noah Mei, Siyuan Gu, Mingxuan Pfaff, Laura Vysotskaya, Nastassia Yu, Haijun Maier, Andreas |
| contents | Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often incorporating temporal smoothness constraints on the estimated motion patterns. This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies. Results demonstrate that the choice of motion model crucially influences recoverable frequencies. The optimization-based motion compensation algorithm is able to accurately fit the spline nodes for frequencies almost up to the node-dependent theoretical limit according to the Nyquist-Shannon theorem. Notably, a higher node count does not compromise reconstruction performance for slow motion patterns, but can extend the range of recoverable high frequencies for the investigated algorithm. Eventually, the optimal motion model is dependent on the imaged anatomy, clinical use case, and scanning protocol and should be tailored carefully to the expected motion frequency spectrum to ensure accurate motion compensation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_19079 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation Thies, Mareike Wagner, Fabian Maul, Noah Mei, Siyuan Gu, Mingxuan Pfaff, Laura Vysotskaya, Nastassia Yu, Haijun Maier, Andreas Image and Video Processing Computer Vision and Pattern Recognition Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often incorporating temporal smoothness constraints on the estimated motion patterns. This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies. Results demonstrate that the choice of motion model crucially influences recoverable frequencies. The optimization-based motion compensation algorithm is able to accurately fit the spline nodes for frequencies almost up to the node-dependent theoretical limit according to the Nyquist-Shannon theorem. Notably, a higher node count does not compromise reconstruction performance for slow motion patterns, but can extend the range of recoverable high frequencies for the investigated algorithm. Eventually, the optimal motion model is dependent on the imaged anatomy, clinical use case, and scanning protocol and should be tailored carefully to the expected motion frequency spectrum to ensure accurate motion compensation. |
| title | On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19079 |