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A study on noise reduction for dual-energy CT material decomposition with autoencoder

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Abstract

Purpose

A major challenge for the material decomposition task of the dual-energy computed tomography (DECT) is the algorithm often suffers from heavy noise in the results. The purpose of this study is to propose a scheme to increase the noise performance of material decomposition.

Methods

The scheme we propose in this paper is to apply an autoencoder-based denoising procedure to the photon-counting DECT images before they are fed into the material decomposition algorithm. We implement the autoencoder (AE) by stacking a series of convolutional and deconvolutional layers. The decomposition technique adopted in our work is an iterative method using least squares estimation with the Huber loss function. The noises of the input and the output of material decomposition are analyzed with both simulated data and real data. Phantom and chicken wing experiments are conducted with a photon-counting-based spectral CT scanner to evaluate the proposed material decomposition scheme.

Results

The noise analysis of the input and the output of material decomposition demonstrates a positive correlation between them. Comparative experiment indicates a noise reduction in the output density maps for 26.07% to 35.65% after the autoencoder pre-processing is applied. The resultant contrast-to-noise ratio is largely increased, correspondingly.

Conclusions

By utilizing the additional autoencoder denoising step, the material decomposition algorithm achieves an improvement in the noise performance of the resultant density maps.

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Acknowledgements

This work is supported by the National Key R&D Program of China (Grant No. 2016YFC0100400), the Instrument Developing Project of the Chinese Academy of Sciences (Grant No. YZ201511) and the Key Technology Research and Development Team Project of Chinese Academy of Sciences (Grant No. GJJSTD2017005).

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Authors and Affiliations

  1. Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Mohan Li, Zhe Wang, Qiong Xu, Zhidu Zhang, Zhiwei Cheng, Shuangquan Liu, Baodong Liu, Cunfeng Wei & Long Wei

  2. School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Mohan Li, Zhe Wang, Qiong Xu, Zhidu Zhang, Zhiwei Cheng, Shuangquan Liu, Baodong Liu, Cunfeng Wei & Long Wei

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  1. Mohan Li
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Correspondence to Cunfeng Wei.

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Li, M., Wang, Z., Xu, Q. et al. A study on noise reduction for dual-energy CT material decomposition with autoencoder. Radiat Detect Technol Methods 3, 44 (2019). https://doi.org/10.1007/s41605-019-0122-2

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  • DOI: https://doi.org/10.1007/s41605-019-0122-2

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