用于肢体成像的半剂量深部少视图高分辨率光子计数CT
Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging
AI总结：
本文提出一种基于深度学习的半剂量高分辨率光子计数CT重建方法，通过块式体积细化网络和合成数据训练，在临床试验中实现剂量减半且不损图像质量。
摘要：
X射线光子计数计算机断层扫描（PCCT）用于肢体成像可实现多能量高分辨率（HR）成像，但其辐射剂量可进一步优化。尽管深度学习技术潜力巨大，但其在高分辨率体积PCCT重建中的应用一直受到大内存负担、训练数据稀缺和域差距问题的挑战。本文提出了一种基于深度学习的方法，用于在半剂量和双倍速度下进行PCCT图像重建，并在新西兰临床试验中得到验证。具体而言，我们设计了一个基于块的体积细化网络以缓解GPU内存限制，使用合成数据训练网络，并利用基于模型的迭代细化来弥合合成数据与临床数据之间的差距。在来自临床试验的8名患者的读者研究中，我们的结果表明，在不影响图像质量和诊断价值的情况下，将辐射剂量削减至临床PCCT标准的一半具有巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
