Black-box optimization of CT acquisition and reconstruction parameters: a reinforcement learning approach

This study presents a novel methodology for optimizing CT protocols using Virtual Imaging Trials (VITs) and reinforcement learning. Computational phantoms with lung disease were imaged using a validated CT simulator and reconstructed with a novel CT reconstruction toolkit. The optimization parameter space included tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. A Proximal Policy Optimization (PPO) agent was trained to minimize the Root Mean Square Error (RMSE) between reconstructed images and ground truth phantoms. The reinforcement learning approach achieved a local minimum RMSE within 1 HU of the absolute minimum RMSE, with 80.5% fewer steps compared to an exhaustive search. This methodology demonstrates a robust and flexible framework for CT protocol optimization that is adaptable to various image quality metrics.