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Proceedings Paper

Feasibility of achieving spectral CT imaging from a single KV acquisition and deep learning method
Author(s): Yinsheng Li; Juan Pablo Cruz-Bastida; Ke Li; Daniel Bushe; Christopher François; Meghan Lubner; Guang-Hong Chen
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131222 (16 March 2020); doi: 10.1117/12.2549611
Show Author Affiliations
Yinsheng Li, Univ. of Wisconsin-Madison (United States)
Juan Pablo Cruz-Bastida, Univ. of Wisconsin-Madison (United States)
Ke Li, Univ. of Wisconsin-Madison (United States)
Daniel Bushe, Univ. of Wisconsin-Madison (United States)
Christopher François, Univ. of Wisconsin-Madison (United States)
Meghan Lubner, Univ. of Wisconsin-Madison (United States)
Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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