End-to-End Deep Learning Improves CT Material Decomposition

Table of Links

Abstract and 1 Introduction

1. Dual-Energy CT Forward Model
2. [Model-based Optimization Problem]()
3. End-to-End Model-based Deep Learning for Material Decomposition (E2E-Decomp)
4. Numerical Results
5. Conclusion
6. Compliance with Ethical Standards and References

4 End-to-End Model-based Deep Learning for Material Decomposition (E2E-Decomp)

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\ 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.

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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.

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\ 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. Numerical results show the effectiveness

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\ of the proposed E2E-DEcomp compared to other supervised approaches since it has fast convergence and excellent performance on low-dose DECT which can lead to further study with clinical dataset.

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7 Compliance with Ethical Standards

This is a numerical simulation study for which no ethical approval was required.

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:::info 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).

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:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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