Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial-猫眼课题宝

DOI：

10.48550/arxiv.2403.12331

发表时间：

2024-03

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Mengzhou Li;Chuang Niu;Ge Wang;Maya R. Amma;Krishna M. Chapagain;Stefan Gabrielson;Andrew Li;Kevin Jonker;Niels J. A. De Ruiter;Jennifer A. Clark;Phillip H. Butler;Anthony Butler;Hengyong Yu

最新的四肢 X 射线光子计数计算机断层扫描 (PCCT) 可实现多能量高分辨率 (HR) 成像，用于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 少视图重建方面取得了成功，但由于 GPU 内存限制、训练数据稀缺和域间隙问题，将其应用于临床诊断四肢扫描的 HR 体积重建仍然受到限制。在本文中，我们在新西兰的一项临床试验中提出了一种基于深度学习的方法，以减半剂量和双倍速度进行 PCCT 图像重建。特别是，我们提出了一种基于补丁的体积细化网络来缓解 GPU 内存限制，使用合成数据训练网络，并使用基于模型的迭代细化来弥合合成数据和真实数据之间的差距。仿真和模型实验表明，使用固定网络在域内和域外结构的不同采集条件下，结果得到持续改进。三位放射科医生对临床试验中 8 名患者的图像质量进行了评估，并与全视图数据集的标准图像重建进行了比较。结果表明，我们提出的方法在诊断图像质量评分方面基本上与临床基准相同或更好。我们的方法具有巨大的潜力，可以在不影响图像质量的情况下提高 PCCT 的安全性和效率。

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.

Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial

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