基于强化学习和虚拟成像试验的CT任务协议优化
Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials
- Duke University（杜克大学）
- Oak Ridge National Laboratory（橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种结合强化学习和虚拟成像试验的CT协议优化框架，通过少量测试即可接近穷举搜索性能，实现任务导向、剂量感知的扫描前协议选择。
AI中文摘要：
计算机断层扫描（CT）中的协议优化旨在提高诊断图像质量的同时降低辐射剂量，但采集参数与重建参数之间的相互依赖性使得穷举测试不切实际。我们提出了一种结合强化学习的虚拟成像试验框架，用于高效的CT协议优化。使用经过验证的CT模拟器，对63个带有肝脏病灶的计算人体模型进行了成像，涵盖了468种采集和重建参数组合，包括管电压、管电流、重建核、层厚和像素尺寸。优化目标在肝脏病灶可检测性（以可检测性指数d-prime量化）与辐射剂量之间取得平衡。训练了一个近端策略优化（Proximal Policy Optimization）智能体，并以从预训练视觉变换器（vision transformer）提取的患者特定CT定位像嵌入为条件。在留出患者上，每位患者仅评估8种协议（约占穷举测试的2%），即恢复了穷举搜索基准目标值的98.2%。在没有患者特定模拟的情况下，仅使用替代评分即可达到89.7%的恢复率。以定位像为条件使零模拟恢复率比不考虑定位像的策略提高了10.7个百分点（配对95%置信区间2.9-19.5；p=0.02）。这些结果表明，所提出的框架能够大幅减少穷举协议测试，同时实现在诊断扫描前进行基于任务且剂量感知的协议选择。
英文摘要：
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.
