Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view. We demonstrate the feasibility of the approach with upper-abdomen, lung, and head-and-neck computed tomography scans from three patients. Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging systems.
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The authors declare that the main data supporting the results in this study are available within the paper and its Supplementary Information. The raw datasets from Stanford Hospital are protected because of patient privacy yet can be made available upon request provided that approval is obtained after an Institutional Review Board procedure at Stanford.
The source code of the deep-learning algorithm is available for research uses at https://github.com/liyues/PatRecon.
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This research is partially supported by the National Institutes of Health (R01CA176553 and R01EB016777). The contents of this article are solely the responsibility of the authors and do not necessarily represent the official NIH views.
The authors declare no competing interests.
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Shen, L., Zhao, W. & Xing, L. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning. Nat Biomed Eng 3, 880–888 (2019). https://doi.org/10.1038/s41551-019-0466-4
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