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. 2024 Mar 15;14(3):2370-2390.
doi: 10.21037/qims-23-922. Epub 2024 Mar 7.

Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility

Zilong Xu  1   2 , Miaomiao Li  3 , Baosheng Li  1   2 , Huazhong Shu  1
Affiliations

Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility

Zilong Xu et al. Quant Imaging Med Surg. .

Abstract

Background: Dual-energy computed tomography (CT) can provide a range of image information beyond conventional CT through virtual monoenergetic images (VMIs). The purpose of this study was to investigate the impact of material decomposition in detector-based spectral CT on radiomics features and effectiveness of using deep learning-based image synthesis to improve the reproducibility of radiomics features.

Methods: In this paper, spectral CT image data from 45 esophageal cancer patients were collected for investigation retrospectively. First, we computed the correlation coefficient of radiomics features between conventional kilovoltage peak (kVp) CT images and VMI. Then, a wavelet loss-enhanced CycleGAN (WLL-CycleGAN) with paired loss terms was developed to synthesize virtual monoenergetic CT images from the corresponding conventional single-energy CT (SECT) images for improving radiomics reproducibility. Finally, the radiomic features in 6 different categories, including gray-level co-occurrence matrix (GLCM), gray-level difference matrix (GLDM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and wavelet, were extracted from the gross tumor volumes from conventional single energy CT, synthetic virtual monoenergetic CT images, and virtual monoenergetic CT images. Comparison between errors in the VMI and synthetic VMI (sVMI) suggested that the performance of our proposed deep learning method improved the radiomic feature accuracy.

Results: Material decomposition of dual-layer dual-energy CT (DECT) can substantially influence the reproducibility of the radiomic features, and the degree of impact is feature dependent. The average reduction of radiomics errors for 15 patients in testing sets was 96.9% for first-order, 12.1% for GLCM, 12.9% for GLDM, 15.7% for GLRLM, 50.3% for GLSZM, 53.4% for NGTDM, and 6% for wavelet features.

Conclusions: The work revealed that material decomposition has a significant effect on the radiomic feature values. The deep learning-based method reduced the influence of material decomposition in VMIs and might improve the robustness and reproducibility of radiomic features in esophageal cancer. Quantitative results demonstrated that our proposed wavelet loss-enhanced paired CycleGAN outperforms the original CycleGAN.

Keywords: Deep learning; detector-based spectral; radiomics; virtual monoenergetic images (VMIs); wavelet loss.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-922/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The workflow of the whole study. CT, computed tomography; WLL, wavelet loss; GAN, generative adversarial network; ROI, region of interest.
Figure 2
Figure 2
The workflow for computing the wavelet loss. VMI, virtual monoenergetic image; sVMI, synthetic virtual monoenergetic image; DWT, discrete wavelet transformation; LL, low pass filter for each row and column (average details); LH, low pass filter for each row and high pass filter for each column (vertical details); HL, high pass filter for each row and low pass filter for each column (horizontal details); HH, high pass filter for each row and column (diagonal details); MSE, mean square error; Normalization, min-max normalization is used in the framework.
Figure 3
Figure 3
Correlation between conventional CT images (i.e., conventional 120 kVp image) and different VMIs. Each cell represents the concordance correlation coefficient between radiomics features of conventional CT images and VMIs. CT, computed tomography; kVp, kilovoltage peak; VMIs, virtual monoenergetic images.
Figure 4
Figure 4
Image and ROI comparison: top row, column 1: conventional CT image; top row, column 2: 40 keV VMI; bottom row, column 1: synthetic 40 keV virtual monoenergetic image via pix2pix model; bottom row, column 2: synthetic 40 keV virtual monoenergetic image via CycleGAN; bottom row, column 3: synthetic 40 keV virtual monoenergetic image via proposed WLL-CycleGAN. All images are from the same slice of one patient. VMI, virtual monoenergetic image; s40 keV VMI, synthetic 40 keV VMI; GAN, generative adversarial network; WLL, wavelet loss; ROI, region of interest; CT, computed tomography.
Figure 5
Figure 5
Image difference comparison among all methods. The images represent the distinction between synthetic virtual monoenergetic images and the virtual monoenergetic image. Top row: whole body comparison; bottom row: ROI comparison. Column 1: the difference between conventional CT images and 40 keV VMI. Column 2: the difference between synthetic 40 keV VMIs using pix2pix model and VMIs. Column 3: the difference between synthetic 40 keV VMIs using CycleGAN and 40 keV VMIs. Column 4: the difference between synthetic 40 keV VMIs using proposed WLL-CycleGAN and 40 keV VMIs. VMI, virtual monoenergetic image; s40 keV VMI, synthetic 40 keV VMI; GAN, generative adversarial network; WLL, wavelet loss; ROI, region of interest; CT, computed tomography.
Figure 6
Figure 6
Histograms of HU values for each case for comparison. s40 keV VMI, synthetic 40 keV VMI; VMI, virtual monoenergetic image; WLL, wavelet loss; HU, Hounsfield unit.
Figure 7
Figure 7
The heatmap shows the CCC comparison of radiomics features extracted from original images. Row 1 indicates the CCC between conventional CT images and virtual monoenergetic images. Row 2 indicates the CCC between synthetic virtual monoenergetic images using CycleGAN and virtual monoenergetic images. Row 3 indicates the CCC between synthetic VMIs using WLL-CycleGAN and virtual monoenergetic images. WLL, wavelet loss; CCC, concordance correlation coefficient; CT, computed tomography; GAN, generative adversarial network; VMIs, virtual monoenergetic images.
Figure 8
Figure 8
Comparison of errors in low-level texture features. Each line represents errors of radiomics features obtained from conventional CT images or synthetic VMIs and VMIs. s40 keV, synthetic 40 keV; GAN, generative adversarial network; WLL, wavelet loss; CT, computed tomography; VMIs, virtual monoenergetic images.
Figure 9
Figure 9
Comparison of errors in wavelet-based features. Each point represents errors of radiomics features obtained from conventional CT images or synthetic VMIs and VMIs. GAN, generative adversarial network; CT, computed tomography; WLL, wavelet loss; VMIs, virtual monoenergetic images.
Figure 10
Figure 10
Image and ROI comparison: top row, column 1: conventional CT image; top row, column 2: 40 keV virtual monoenergetic image; bottom row, column 1: synthetic conventional CT image via pix2pix model; bottom row, column 2: synthetic conventional CT image via CycleGAN; bottom row, column 3: synthetic conventional CT image via proposed WLL-CycleGAN. All images are from the same slice of one patient. VMI, virtual monoenergetic image; sConv, synthetic conventional CT image; GAN, generative adversarial network; WLL, wavelet loss; ROI, region of interest; CT, computed tomography.
Figure 11
Figure 11
Comparison of image differences among all methods. The images represent the distinction between synthetic virtual monoenergetic images and the virtual monoenergetic images. Top row: whole body comparison; bottom row: ROI comparison. Column 1: the difference between conventional CT images and 40 keV VMI. Column 2: the difference between synthetic conventional CT images using pix2pix model and conventional CT images. Column 3: the difference between synthetic conventional CT images using CycleGAN and conventional CT images. Column 4: the difference between synthetic conventional CT images using proposed WLL-CycleGAN and conventional CT images. VMI, virtual monoenergetic image; sConv, synthetic conventional CT image; GAN, generative adversarial network; WLL, wavelet loss; ROI, region of interest; CT, computed tomography.
Figure 12
Figure 12
Histograms of HU values for each case for comparison (from 40 keV to conventional). sConv, synthetic conventional CT image; GAN, generative adversarial network; WLL, wavelet loss; VMI, virtual monoenergetic image; HU, Hounsfield unit; CT, computed tomography.
Figure 13
Figure 13
Error comparison of low-level texture features for the synthesis from 40 keV VMI to conventional CT images (from 40 keV to conventional). sConv, synthetic conventional CT image; GAN, generative adversarial network; WLL, wavelet loss; VMI, virtual monoenergetic image; CT, computed tomography.
Figure 14
Figure 14
Error comparison of wavelet-based features (from 40 keV to conventional). sConv, synthetic conventional CT image; GAN, generative adversarial network; WLL, wavelet loss; CT, computed tomography.
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