Deep reinforcement learning for optimising angle selection and dose allocation in CT reconstruction
Applied Mathematics in Science and Engineering, vol. 34
Abstract
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.
Authors 5
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Leiden University · Centrum Wiskunde & Informatica
Affiliation as printed
Centrum Wiskunde & Informatica
Leiden Institute of Advanced Computer Science, Leiden University
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden University
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Centrum Wiskunde & Informatica
Affiliation as printed
Centrum Wiskunde & Informatica
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Centrum Wiskunde & Informatica · Utrecht University
Affiliation as printed
Centrum Wiskunde & Informatica
Mathematics Institute, Utrecht University
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Leiden University · Centrum Wiskunde & Informatica
Affiliation as printed
Centrum Wiskunde & Informatica
Leiden Institute of Advanced Computer Science, Leiden University
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