X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed validated in a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value.

翻译：用于四肢的X射线光子计数计算机断层扫描（PCCT）能够实现多能量高分辨率（HR）成像，但其辐射剂量仍有进一步优化的空间。尽管深度学习技术具有巨大潜力，但其在高分辨率三维PCCT重建中的应用一直受到内存负担大、训练数据稀缺以及领域差异等问题的挑战。本文提出一种基于深度学习的方法，用于实现剂量减半、速度加倍的光子计数CT图像重建，该方法已在一项新西兰临床试验中得到验证。具体而言，我们设计了一种基于图像块的三维精细化网络以缓解GPU内存限制，使用合成数据训练网络，并采用基于模型的迭代优化方法来弥合合成数据与临床数据之间的差异。我们对临床试验中8名患者进行的阅片研究结果表明，该方法在保持图像质量和诊断价值的前提下，具有将辐射剂量降低至临床光子计数CT标准剂量一半的巨大潜力。