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17 October 2022 X-ray dissectography enables stereotography
Chuang Niu, Ge Wang
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123041T (2022) https://doi.org/10.1117/12.2647278
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
X-ray imaging is the most popular medical imaging technology. While X-ray radiography is rather cost-effective, tissue structures are superimposed along the X-ray paths. On the other hand, computed tomography (CT) reconstructs internal structures but CT increases radiation dose, is complicated and expensive. Here we propose ”X-ray dissectography” to extract a target organ digitally from few radiographic projections for stereographic and tomographic analysis in the deep learning framework. As an exemplary embodiment, we propose a general X-ray dissectography network, a dedicated X-ray stereotography network, and the X-ray imaging systems to implement these functionalities. Our experiments show that X-ray stereography can be achieved of an isolated organ such as the lungs in this case, suggesting the feasibility of transforming conventional radiographic reading to the stereographic examination of the isolated organ, which potentially allows higher sensitivity and specificity, and even tomographic visualization of the target. With further improvements, X-ray dissectography promises to be a new X-ray imaging modality for CT-grade diagnosis at radiation dose and system cost comparable to that of radiographic or tomosynthetic imaging.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Chuang Niu and Ge Wang "X-ray dissectography enables stereotography", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123041T (17 October 2022); https://doi.org/10.1117/12.2647278
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KEYWORDS
X-rays
Radiography
X-ray imaging
3D acquisition
Lung
Image segmentation
Imaging systems
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