Abstract：
Objective.The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy.Approach.Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints.Main results.The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods.Significance.The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model\'s generalizability suggests it has the potential applicable in various healthcare settings.
摘要：
Objective.这项研究的目的是从超稀疏的二维X射线投影实时重建体积计算机断层扫描（CT）图像，在图像引导放射治疗期间促进更容易的导航和定位。方法。我们的方法利用体素-sapce搜索变压器模型来克服传统CT重建技术的局限性，这需要大量的X射线投影，并导致高辐射剂量和设备限制。主要结果。提出的XTransCT算法在图像质量方面表现出卓越的性能，结构精度,以及跨不同数据集的通用化，包括医院的50名病人,大规模公共LIDC-IDRI数据集，和LNDb数据集进行交叉验证。值得注意的是,该算法的重建速度提高了约300%，与以前的基于3D卷积的方法相比，每个3D图像重建的速率为44毫秒。意义。XTransCT架构有可能通过更快地提供高质量的CT图像并大大减少患者的辐射暴露来影响临床实践。该模型的普适性表明它有可能适用于各种医疗保健环境。
