Journal
NATURE BIOMEDICAL ENGINEERING
Volume 3, Issue 11, Pages 880-888Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41551-019-0466-4
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Funding
- National Institutes of Health [R01CA176553, R01EB016777]
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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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