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A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images.
Med Phys 2019; 46(9):3906-3923MP

Abstract

PURPOSE

Convolutional neural network (CNN)-based image denoising techniques have shown promising results in low-dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel-level loss function. Perceptual loss and adversarial loss have been proposed recently to further improve the image denoising performance. In this paper, we investigate the effect of different loss functions on image denoising performance using task-based image quality assessment methods for various signals and dose levels.

METHODS

We used a modified version of U-net that was effective at reducing the correlated noise in CT images. The loss functions used for comparison were two pixel-level losses (i.e., the mean-squared error and the mean absolute error), Visual Geometry Group network-based perceptual loss (VGG loss), adversarial loss used to train the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), and their weighted summation. Each image denoising method was applied to reconstructed images and sinogram images independently and validated using the extended cardiac-torso (XCAT) simulation and Mayo Clinic datasets. In the XCAT simulation, we generated fan-beam CT datasets with four different dose levels (25%, 50%, 75%, and 100% of a normal-dose level) using 10 XCAT phantoms and inserted signals in a test set. The signals had two different shapes (spherical and spiculated), sizes (4 and 12 mm), and contrast levels (60 and 160 HU). To evaluate signal detectability, we used a detection task SNR (tSNR) calculated from a non-prewhitening model observer with an eye filter. We also measured the noise power spectrum (NPS) and modulation transfer function (MTF) to compare the noise and signal transfer properties.

RESULTS

Compared to CNNs without VGG loss, VGG-loss-based CNNs achieved a more similar tSNR to that of the normal-dose CT for all signals at different dose levels except for a small signal at the 25% dose level. For a low-contrast signal at 25% or 50% dose, adding other losses to the VGG loss showed more improved performance than only using VGG loss. The NPS shapes from VGG-loss-based CNN closely matched that of normal-dose CT images while CNN without VGG loss overly reduced the mid-high-frequency noise power at all dose levels. MTF also showed VGG-loss-based CNN with better-preserved high resolution for all dose and contrast levels. It is also observed that additional WGAN-GP loss helps improve the noise and signal transfer properties of VGG-loss-based CNN.

CONCLUSIONS

The evaluation results using tSNR, NPS, and MTF indicate that VGG-loss-based CNNs are more effective than those without VGG loss for natural denoising of low-dose images and WGAN-GP loss improves the denoising performance of VGG-loss-based CNNs, which corresponds with the qualitative evaluation.

Authors+Show Affiliations

School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, 21983, South Korea.School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, 21983, South Korea.School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, 21983, South Korea.School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, Incheon, 21983, South Korea.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31306488

Citation

Kim, Byeongjoon, et al. "A Performance Comparison of Convolutional Neural Network-based Image Denoising Methods: the Effect of Loss Functions On Low-dose CT Images." Medical Physics, vol. 46, no. 9, 2019, pp. 3906-3923.
Kim B, Han M, Shim H, et al. A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images. Med Phys. 2019;46(9):3906-3923.
Kim, B., Han, M., Shim, H., & Baek, J. (2019). A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images. Medical Physics, 46(9), pp. 3906-3923. doi:10.1002/mp.13713.
Kim B, et al. A Performance Comparison of Convolutional Neural Network-based Image Denoising Methods: the Effect of Loss Functions On Low-dose CT Images. Med Phys. 2019;46(9):3906-3923. PubMed PMID: 31306488.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images. AU - Kim,Byeongjoon, AU - Han,Minah, AU - Shim,Hyunjung, AU - Baek,Jongduk, Y1 - 2019/08/06/ PY - 2019/03/25/received PY - 2019/07/03/revised PY - 2019/07/05/accepted PY - 2019/7/16/pubmed PY - 2019/7/16/medline PY - 2019/7/16/entrez KW - adversarial loss KW - deep learning KW - feature-level loss KW - image denoising KW - low-dose CT KW - mathematical observer KW - modulation transfer function KW - noise power spectrum SP - 3906 EP - 3923 JF - Medical physics JO - Med Phys VL - 46 IS - 9 N2 - PURPOSE: Convolutional neural network (CNN)-based image denoising techniques have shown promising results in low-dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel-level loss function. Perceptual loss and adversarial loss have been proposed recently to further improve the image denoising performance. In this paper, we investigate the effect of different loss functions on image denoising performance using task-based image quality assessment methods for various signals and dose levels. METHODS: We used a modified version of U-net that was effective at reducing the correlated noise in CT images. The loss functions used for comparison were two pixel-level losses (i.e., the mean-squared error and the mean absolute error), Visual Geometry Group network-based perceptual loss (VGG loss), adversarial loss used to train the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), and their weighted summation. Each image denoising method was applied to reconstructed images and sinogram images independently and validated using the extended cardiac-torso (XCAT) simulation and Mayo Clinic datasets. In the XCAT simulation, we generated fan-beam CT datasets with four different dose levels (25%, 50%, 75%, and 100% of a normal-dose level) using 10 XCAT phantoms and inserted signals in a test set. The signals had two different shapes (spherical and spiculated), sizes (4 and 12 mm), and contrast levels (60 and 160 HU). To evaluate signal detectability, we used a detection task SNR (tSNR) calculated from a non-prewhitening model observer with an eye filter. We also measured the noise power spectrum (NPS) and modulation transfer function (MTF) to compare the noise and signal transfer properties. RESULTS: Compared to CNNs without VGG loss, VGG-loss-based CNNs achieved a more similar tSNR to that of the normal-dose CT for all signals at different dose levels except for a small signal at the 25% dose level. For a low-contrast signal at 25% or 50% dose, adding other losses to the VGG loss showed more improved performance than only using VGG loss. The NPS shapes from VGG-loss-based CNN closely matched that of normal-dose CT images while CNN without VGG loss overly reduced the mid-high-frequency noise power at all dose levels. MTF also showed VGG-loss-based CNN with better-preserved high resolution for all dose and contrast levels. It is also observed that additional WGAN-GP loss helps improve the noise and signal transfer properties of VGG-loss-based CNN. CONCLUSIONS: The evaluation results using tSNR, NPS, and MTF indicate that VGG-loss-based CNNs are more effective than those without VGG loss for natural denoising of low-dose images and WGAN-GP loss improves the denoising performance of VGG-loss-based CNNs, which corresponds with the qualitative evaluation. SN - 2473-4209 UR - https://www.unboundmedicine.com/medline/citation/31306488/A_performance_comparison_of_convolutional_neural_network_based_image_denoising_methods:_The_effect_of_loss_functions_on_low_dose_CT_images_ L2 - https://doi.org/10.1002/mp.13713 DB - PRIME DP - Unbound Medicine ER -