Feasibility Of Achieving Spectral Ct Imaging From A Single Kv Acquisition And Deep Learning Method

CT imaging is one of the primary diagnostic tools utilized in modern radiology departments. Current state-of-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.