端到端优化降低CT扫描剂量，实现辐射安全与图像质量平衡
End2end-ALARA: Approaching the ALARA Law in CT Imaging with End-to-end Learning
- 端到端联合优化剂量调节与图像重建，通过可微模拟连接模块
- 在保持相同图像质量下，剂量比传统方法降低30%以上
- 适合临床CT系统升级、医学AI模型训练数据采集
CT检查会给患者带来辐射伤害。行业共识是遵循辐射防护最优化原则（ALARA），即在合理可行范围内尽可能降低辐射剂量。本文提出一种端到端学习框架End2end-ALARA，通过构建剂量调节模块与图像重建模块，并以可微分模拟函数连接，结合约束型铰链损失函数联合优化，目标是在满足预设图像质量（IQ）指标的前提下最小化辐射剂量。实验表明，End2end-ALARA能为不同患者设定个性化低剂量水平，维持稳定的图像质量，有利于基于影像的诊断和下游模型训练。相较于固定剂量及传统剂量调节策略，该方法在同等图像质量下显著降低辐射剂量。
原文摘要 · Abstract (English)
Computed tomography (CT) examination poses radiation injury to patient. A consensus performing CT imaging is to make the radiation dose as low as reasonably achievable, i.e. the ALARA law. In this paper, we propose an end-to-end learning framework, named End2end-ALARA, that jointly optimizes dose modulation and image reconstruction to meet the goal of ALARA in CT imaging. End2end-ALARA works by building a dose modulation module and an image reconstruction module, connecting these modules with a differentiable simulation function, and optimizing the them with a constrained hinge loss function. The objective is to minimize radiation dose subject to a prescribed image quality (IQ) index. The results show that End2end-ALARA is able to preset personalized dose levels to gain a stable IQ level across patients, which may facilitate image-based diagnosis and downstream model training. Moreover, compared to fixed-dose and conventional dose modulation strategies, End2end-ALARA consumes lower dose to reach the same IQ level. Our study sheds light on a way of realizing the ALARA law in CT imaging.
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