Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917430324887552 |
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| author | Wang, Tianyuan Pelt, Daniël M. Lucka, Felix van Leeuwen, Tristan Batenburg, K. Joost |
| author_facet | Wang, Tianyuan Pelt, Daniël M. Lucka, Felix van Leeuwen, Tristan Batenburg, K. Joost |
| contents | Traditional X-ray computed tomography (CT) scanning strategies typically select projection angles uniformly and allocate dose equally. In practice, however, CT scans often need to be fast, radiation-efficient, and adaptive. Sparse-view tomography addresses these requirements by reducing both the number of angles and the total dose budget. Under such constraints, angle selection and dose allocation should be information-driven, with more dose assigned to informative directions. To this end, we propose a dose-aware acquisition and reconstruction framework that combines a PWLS-PnP reconstruction backbone with an RL-based strategy for adaptive angle selection, explicitly accounting for angle-dependent photon statistics. Numerical experiments show that the proposed approach improves overall reconstruction quality and enhances defect detectability compared with conventional strategies, particularly when only a small number of projections or a constrained dose budget is available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20939 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction Wang, Tianyuan Pelt, Daniël M. Lucka, Felix van Leeuwen, Tristan Batenburg, K. Joost Medical Physics Traditional X-ray computed tomography (CT) scanning strategies typically select projection angles uniformly and allocate dose equally. In practice, however, CT scans often need to be fast, radiation-efficient, and adaptive. Sparse-view tomography addresses these requirements by reducing both the number of angles and the total dose budget. Under such constraints, angle selection and dose allocation should be information-driven, with more dose assigned to informative directions. To this end, we propose a dose-aware acquisition and reconstruction framework that combines a PWLS-PnP reconstruction backbone with an RL-based strategy for adaptive angle selection, explicitly accounting for angle-dependent photon statistics. Numerical experiments show that the proposed approach improves overall reconstruction quality and enhances defect detectability compared with conventional strategies, particularly when only a small number of projections or a constrained dose budget is available. |
| title | Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2604.20939 |