PDF(Pubmed)
Abstract：
The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, 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 in a New Zealand clinical trial. Particularly, we present 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 real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality.
摘要：
最新的四肢X射线光子计数计算机断层扫描（PCCT）允许多能量高分辨率（HR）成像的组织表征和材料分解。然而,对于对比增强和其他研究，辐射剂量和成像速度都需要改进。尽管深度学习方法在2D少视重建方面取得了成功，由于GPU内存限制，将它们应用于四肢扫描的HR体积重建以进行临床诊断受到限制，培训数据稀缺，和领域差距问题。在本文中,在一项新西兰临床试验中,我们提出了一种基于深度学习的PCCT图像重建方法,剂量减半,速度加倍.特别是,我们提出了一个基于补丁的体积细化网络来缓解GPU内存限制，具有合成数据的火车网络，并使用基于模型的迭代细化来弥合合成数据和现实数据之间的差距。仿真和体模实验证明，在使用固定网络的域内和域外结构上，在不同的采集条件下，结果得到了一致的改善。与具有全视图数据集的标准图像重建相比，三名放射科医生评估了来自临床试验的8名患者的图像质量。表明，就诊断图像质量评分而言，我们提出的方法与临床基准基本相同或更好。我们的方法具有在不影响图像质量的情况下提高PCCT的安全性和效率的巨大潜力。
