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. To
guarantee the replicability of our results, we have made our code publicly
available.

本文提出了一种名为 XTransCT 的新型 Transformer 架构，可从二维 X 光图像中实时重建 CT 图像，该方法在图像质量、结构可靠性和普适性方面优于其他方法，并且可以在重建 3D 图像的同时实现约 300％的速度提高。