Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction

TL;DR

This paper introduces a deep reinforcement learning framework for adaptive angle selection and dose allocation in CT reconstruction, improving image quality under limited projections and dose constraints.

Contribution

It presents a novel RL-based strategy integrated with a PWLS-PnP reconstruction backbone for information-driven, dose-aware CT acquisition.

Findings

Improves reconstruction quality compared to conventional methods.

Enhances defect detectability with fewer projections.

Effective under strict dose constraints.

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.