Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials

2026-09-10

AI Summary

研究使用强化学习和虚拟成像试验优化CT协议，平衡肝脏病变检测能力和辐射剂量，大幅减少测试量同时实现任务导向的剂量感知协议选择。

Abstract

Protocol optimization in computed tomography (CT) aims to improve diagnostic image quality while reducing radiation dose, but the interdependence of acquisition and reconstruction parameters makes exhaustive testing impractical. We propose a virtual imaging trial framework with reinforcement learning for efficient CT protocol optimization. Sixty-three computational human models with liver lesions were imaged using a validated CT simulator across 468 combinations of acquisition and reconstruction parameters, including tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. The optimization objective balanced liver lesion detectability, quantified by detectability index d-prime, against radiation dose. A Proximal Policy Optimization agent was trained and conditioned on patient-specific CT localizer embeddings derived from a pretrained vision transformer. On held-out patients, evaluating only 8 protocols per patient, about 2% of exhaustive testing, recovered 98.2% of the exhaustive-search oracle objective. With no patient-specific simulation, surrogate scoring alone achieved 89.7% recovery. Conditioning on the localizer improved zero-simulation recovery by 10.7 percentage points over the localizer-blind policy (paired 95% CI 2.9-19.5; p=0.02). These results show that the proposed framework can substantially reduce exhaustive protocol testing while enabling task-based, dose-aware protocol selection before the diagnostic scan.

Problem

• Computed Tomography
• Protocol Optimization
• Radiation Dose
• Image Quality
• Reinforcement Learning

Innovation

• Reinforcement Learning
• Virtual Imaging Trials
• Proximal Policy Optimization
• Vision Transformer
• Task-based Optimization