Siqi Zhu, Jue Wang, Yufang Cai. Low-Dose CT Denoising Algorithm Based on Improved Cycle GAN[J]. Acta Optica Sinica, 2020, 40(22): 2210002
- Acta Optica Sinica
- Vol. 40, Issue 22, 2210002 (2020)
Fig. 1. Cycle generative adversarial network model
Fig. 2. Feature converter residual structure network model. (a) ResNet network model; (b) DenseNet network model
Fig. 3. Generator network training model based on DenseNet
Fig. 4. 5-layer DenseNet network model
Fig. 5. Discriminator network training model
Fig. 6. Algorithm flowchart
Fig. 7. Validation scheme of proposed method
Fig. 8. Denoising results of an image for patient No.48. (a) LDCT; (b) SDCT; (c) ST-NLM processed LDCT; (d) Res-CycleGAN processed LDCT; (e) Dense-CycleGAN processed LDCT
Fig. 9. Automatic calcification score of a computed tomography image. (a) SDCT Agatston score; (b) ST-NLM Agatston score; (c) Res-CycleGAN Agatston score; (d) Dense-CycleGAN Agatston score
Fig. 10. Denoising results of an image for patient No.79. (a) LDCT after adding noise; (b) original image; (c) ST-NLM; (d) Res-CycleGAN; (e) Dense-CycleGAN
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Table 1. Model parameter
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Table 2. Comparison of image performance indicators
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