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Proceedings Paper

Harnessing the power of deep learning for volumetric CT imaging with single or limited number of projections
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Paper Abstract

Tomographic imaging using a penetrating wave, such as X-ray, light and microwave, is a fundamental approach to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object and plays an important role in modern science. To obtain an image free of aliasing artifacts, a sufficiently dense angular sampling that satisfies the Shannon-Nyquist criterion is required. In the past two decades, image reconstruction strategy with sparse sampling has been investigated extensively using approaches such as compressed-sensing. This type of approach is, however, ad hoc in nature as it encourages certain form of images. Recent advancement in deep learning provides an enabling tool to transform the way that an image is constructed. Along this line, Zhu et al1 presented a data-driven supervised learning framework to relate the sensor and image domain data and applied the method to magnetic resonance imaging (MRI). Here we investigate a deep learning strategy of tomographic X-ray imaging in the limit of a single-view projection data input. For the first time, we introduce the concept of dimension transformation in image feature domain to facilitate volumetric imaging by using a single or multiple 2D projections. The mechanism here is fundamentally different from the traditional approaches in that the image formation is driven by prior knowledge casted in the deep learning model. This work pushes the boundary of tomographic imaging to the single-view limit and opens new opportunities for numerous practical applications, such as image guided interventions and security inspections. It may also revolutionize the hardware design of future tomographic imaging systems

Paper Details

Date Published: 1 March 2019
PDF: 10 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094826 (1 March 2019); doi: 10.1117/12.2513032
Show Author Affiliations
Liyue Shen, Stanford Univ. (United States)
Wei Zhao, Stanford Univ. (United States)
Lei Xing, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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