Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Dual-Energy CT Forward Model Model-based Optimization Problem End-to-End Model-based Deep Learning for Material Decomposition (E2E-Decomp) Numerical Results Conclusion Compliance with Ethical Standards and References Dual-Energy CT Forward Model Dual-Energy CT Forward Model Model-based Optimization Problem Model-based Optimization Problem End-to-End Model-based Deep Learning for Material Decomposition (E2E-Decomp) End-to-End Model-based Deep Learning for Material Decomposition (E2E-Decomp) Numerical Results Numerical Results Conclusion Conclusion Compliance with Ethical Standards and References Compliance with Ethical Standards and References 4 End-to-End Model-based Deep Learning for Material Decomposition (E2E-Decomp) The workflow of the E2E-DEcomp algorithm at inference is shown in Fig. 1, and the structure of the E2EDEcomp algorithm for inference is reported in Table 1. 5 Numerical Results In order to reduce the number of learnable parameters we utilise the same architecture for the denoising module D at each iteration k with shared parameters ρ. In Fig. 2 it is shown the qualitative comparison on a test material image of the adipose tissue using filtered back projection (FBP) and E2E-DEcomp while in Fig. 3 is is reported the PSNR error for a set of 10 testing images for the 2 material decomposition. It is worth noting that the improvement in the decomposition accuracy are consistent, around 5 dB, across different levels of dose, i.e. from sparse views to higher number of projections. We have also compared the E2E-DEcomp framework with the FBP ConvNet method Jin et al. [2017] and Fig. 4 shows how E2E-DEcomp can achieve a faster convergence in training using fewer epochs. 6 Conclusion This work proposed a direct method for DECT material decomposition using a model-based optimization able to decouple the learning in the measurement and image domain. 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Authors: Authors: (1) Jiandong Wang, Shenzhen Xilaiheng Medical Electronics, (HORRON), China and Centre for Medical Engineering and Technology, University of Dundee, DD1 4HN, UK (jack@horron.com); (2) Alessandro Perelli, Centre for Medical Engineering and Technology, University of Dundee, DD1 4HN, UK (aperelli001@dundee.ac.uk). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv
