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

Liyue Shen; Wei Zhao; Lei Xing

doi:10.1117/12.2513032

1 March 2019 Harnessing the power of deep learning for volumetric CT imaging with single or limited number of projections

Liyue Shen, Wei Zhao, Lei Xing

Author Affiliations +

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 1094826 (2019) https://doi.org/10.1117/12.2513032
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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

Citation Download Citation

Liyue Shen, Wei Zhao, and Lei Xing "Harnessing the power of deep learning for volumetric CT imaging with single or limited number of projections", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094826 (1 March 2019); https://doi.org/10.1117/12.2513032

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