Computed tomography (CT) scans offer a detailed, three-dimensional representation of patients' internal organs. However, conventional CT reconstruction techniques necessitate acquiring hundreds or thousands of x-ray projections through a complete rotational scan of the body, making navigation or positioning during surgery infeasible. In image-guided radiation therapy, a method that reconstructs ultra-sparse X-ray projections into CT images, we can exploit the substantially reduced radiation dose and minimize equipment burden for localization and navigation. In this study, we introduce a novel Transformer architecture, termed XTransCT, devised to facilitate real-time reconstruction of CT images from two-dimensional X-ray images. We assess our approach regarding image quality and structural reliability using a dataset of fifty patients, supplied by a hospital, as well as the larger public dataset LIDC-IDRI, which encompasses thousands of patients. Additionally, we validated our algorithm's generalizability on the LNDb dataset. Our findings indicate that our algorithm surpasses other methods in image quality, structural precision, and generalizability. Moreover, in comparison to previous 3D convolution-based approaches, we note a substantial speed increase of approximately 300 %, achieving 44 ms per 3D image reconstruction.

翻译：计算机断层扫描（CT）可提供患者内部器官的详细三维表征。然而，传统CT重建技术需要通过对身体进行完整旋转扫描来获取数百或数千个X射线投影，这使得手术过程中的导航或定位变得不可行。在图像引导放射治疗中，一种将超稀疏X射线投影重建为CT图像的方法，能够利用大幅降低的辐射剂量，并最小化定位与导航所需的设备负担。本研究提出了一种名为XTransCT的新型Transformer架构，旨在实现从二维X射线图像实时重建CT图像。我们使用医院提供的五十名患者数据集以及包含数千名患者的更大规模公开数据集LIDC-IDRI，从图像质量和结构可靠性方面评估了该方法。此外，我们还在LNDb数据集上验证了算法的泛化能力。结果表明，我们的算法在图像质量、结构精度和泛化能力方面均优于其他方法。与以往基于三维卷积的方法相比，我们注意到速度提升约300%，每次三维图像重建仅需44毫秒。