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Computed tomography (CT) scans offer a detailed, three-dimensionalrepresentation of patients' internal organs. However, conventional CTreconstruction techniques necessitate acquiring hundreds or thousands of x-rayprojections through a complete rotational scan of the body, making navigationor positioning during surgery infeasible. In image-guided radiation therapy, amethod that reconstructs ultra-sparse X-ray projections into CT images, we canexploit the substantially reduced radiation dose and minimize equipment burdenfor localization and navigation. In this study, we introduce a novelTransformer architecture, termed XTransCT, devised to facilitate real-timereconstruction of CT images from two-dimensional X-ray images. We assess ourapproach regarding image quality and structural reliability using a dataset offifty patients, supplied by a hospital, as well as the larger public datasetLIDC-IDRI, which encompasses thousands of patients. Additionally, we validatedour algorithm's generalizability on the LNDb dataset. Our findings indicatethat our algorithm surpasses other methods in image quality, structuralprecision, and generalizability. Moreover, in comparison to previous 3Dconvolution-based approaches, we note a substantial speed increase ofapproximately 300 %, achieving 44 ms per 3D image reconstruction.

XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal  X-Ray Projections for Image-guided Radiation Therapy via a Transformer  Network