Feasibility of achieving spectral CT imaging from a single KV acquisition and deep learning method

Yinsheng Li; Juan Pablo Cruz-Bastida; Ke Li; Daniel Bushe; Christopher François; Meghan Lubner; Guang-Hong Chen

doi:10.1117/12.2549611

16 March 2020 Feasibility of achieving spectral CT imaging from a single KV acquisition and deep learning method

Yinsheng Li, Juan Pablo Cruz-Bastida, Ke Li, Daniel Bushe, Christopher François, Meghan Lubner, Guang-Hong Chen

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 1131222 (2020) https://doi.org/10.1117/12.2549611
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

CT imaging is one of the primary diagnostic tools utilized in modern radiology departments. Current stateof- the-art spectral CT imaging systems have been implemented using advanced x-ray source and/or detector technologies that have enabled image objects to be rapidly scanned using two distinct x-ray spectra (i.e., different effective beam energies). In this paper, we study the possibility to extract the encoded spectral information from the measured data when a single polychromatic x-ray spectrum is used to acquire data using an energy integration detector. Based upon our physical analysis, a physics-based deep neural network architecture, termed the Deep Spectral Imaging Network, was trained to demonstrate the feasibility of achieving spectral CT imaging using an energy integration detector and a single-kV acquisition.

Citation Download Citation

Yinsheng Li, Juan Pablo Cruz-Bastida, Ke Li, Daniel Bushe, Christopher François, Meghan Lubner, and Guang-Hong Chen "Feasibility of achieving spectral CT imaging from a single KV acquisition and deep learning method", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131222 (16 March 2020); https://doi.org/10.1117/12.2549611

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