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Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning

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Abstract

Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner. In this study, we investigated whether a low-to-high kV mapping was better than a high-to-low kV mapping. We used a U-Net model to perform conversions between different kV CT images. Moreover, we proposed a double U-Net model to improve the quality of original single-energy CT images. Ninety-eight patients who underwent brain DECT scans were used to train, validate, and test the proposed DL-based model. The results showed that the low-to-high kV conversion was better than the high-to-low kV conversion. In addition, the DL-based DECT images had better signal-to-noise ratios (SNRs) than the true (original) DECT images, but at the expense of a slight loss in spatial resolution. The mean CT number differences between the true and DL-based DECT images were within \(\pm\) 1 HU. No statistically significant difference in CT number measurements was found between the true and DL-based DECT images (p > 0.05). The DL-based DECT images with improved SNR could produce low-noise virtual monoenergetic images. Our preliminary results indicate that DL has the potential to generate brain DECT images using single-energy brain CT images.

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Funding

This work was supported by MOST 109-2221-E-002-049 from Ministry of Science Technology, Taiwan.

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Authors and Affiliations

  1. Department of Medical Imaging, Changhua Christian Hospital, 135 Nanxiao St, 500, Changhua County, Taiwan

    Chi-Kuang Liu

  2. Department of Biomedical Engineering, University of California, Davis, CA, 95616, USA

    Chih-Chieh Liu

  3. Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., 100, Taipei City, Taiwan

    Cheng-Hsun Yang & Hsuan-Ming Huang

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  1. Chi-Kuang Liu
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  4. Hsuan-Ming Huang
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Correspondence to Hsuan-Ming Huang.

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Appendix

Appendix

Fig. 11
figure 11

An example of true (left column) and filtered (middle column) DECT images, and difference images (right column) between the true and filtered DECT images. The filtered DECT images were the true DECT images with a 2D Gaussian filter (GF) and sigma = 1. All CT images are displayed in a window width of 80 HU and a window level (center) of 40 HU

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Fig. 12
figure 12

An example of true (left column) and filtered (middle column) DECT images, and difference images (right column) between the true and filtered DECT images. The filtered DECT images were the true DECT images with a 2D Gaussian filter (GF) and sigma = 1. All CT images are displayed in a window width of 80 HU and a window level (center) of 40 HU

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Fig. 13
figure 13

An example of true (left column) and filtered (middle column) DECT images, and difference images (right column) between the true and filtered DECT images. The filtered DECT images were the true DECT images with a block-matching and three-dimensional filtering (BM3D) and sigma = 20. All CT images are displayed in a window width of 80 HU and a window level (center) of 40 HU

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Fig. 14
figure 14

An example of true (left column) and filtered (middle column) DECT images, and difference images (right column) between the true and filtered DECT images. The filtered DECT images were the true DECT images with a block-matching and three-dimensional filtering (BM3D) and sigma = 20. All CT images are displayed in a window width of 80 HU and a window level (center) of 40 HU

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Fig. 15
figure 15

k‐Space energy density of true and DL-based a 80-kV and b 140-kV CT images (Fig. 5) as a function of the distance to the k‐space center. The results indicated that the spatial resolution of DL-based DECT images was slightly degraded as compared to that of true DECT images. However, the loss of spatial resolution observed in the DL-based DECT images was less severe than that observed in the true DECT images with a 2D Gaussian filter (GF) and sigma = 1. Moreover, a similar degradation in the spatial resolution was observed when comparing the proposed DL-based DECT images with the true DECT images denoised using a block-matching and three-dimensional filtering (BM3D) and sigma = 20

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Liu, CK., Liu, CC., Yang, CH. et al. Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning. J Digit Imaging 34, 149–161 (2021). https://doi.org/10.1007/s10278-020-00414-1

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  • DOI: https://doi.org/10.1007/s10278-020-00414-1

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