Abstract
Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.
References
D. J. Brenner, E. J. Hall, Computed tomography–an increasing source of radiation exposure, New England Journal of Medicine 357 (22) (2007) 2277–2284.
A. B. de Gonzalez, S. Darby, Risk of cancer from diagnostic x-rays: estimates for the uk and 14 other countries, The lancet 363 (9406) (2004) 345–351.
J. Wang, H. Lu, T. Li, Z. Liang, Sinogram noise reduction for low-dose CT by statistics-based nonlinear filters, in: Medical Imaging 2005: Image Processing, Vol. 5747, SPIE, 2005, pp. 2058–2066.
J. Wang, T. Li, H. Lu, Z. Liang, Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography, IEEE Transactions on Medical Imaging 25 (10) (2006) 1272–1283.
K. S. Sidhu, B. S. Khaira, I. S. Virk, Medical image denoising in the wavelet domain using haar and db3 filtering, International Refereed Journal of Engineering and Science 1 (1) (2012) 001–008.
A. M. Abdulazeez, D. Q. Zeebaree, D. M. Abdulqader, Wavelet applications in medical images: A review, Transform. DWT 21 (2020) 22.
S. Pani, S. C. Saifuddin, F. I. Ferreira, N. Henthorn, P. Seller, P. J. Sellin, P. Stratmann, M. C. Veale, M. D. Wilson, R. J. Cernik, High energy resolution hyperspectral x-ray imaging for low-dose contrast-enhanced digital mammography, IEEE Transactions on Medical Imaging 36 (9) (2017) 1784–1795.
A. M. Hasan, A. Melli, K. A. Wahid, P. Babyn, Denoising low-dose CT images using multiframe blind source separation and block matching filter, IEEE Transactions on Radiation and Plasma Medical Sciences 2 (4) (2018) 279–287.
J. Nuyts, B. De Man, J. A. Fessler, W. Zbijewski, F. J. Beekman, Modelling the physics in the iterative reconstruction for transmission computed tomography, Physics in Medicine & Biology 58 (12) (2013) R63.
Z. Huang, Z. Liu, P. He, Y. Ren, S. Li, Y. Lei, D. Luo, D. Liang, D. Shao, Z. Hu, et al., Segmentation-guided denoising network for low-dose CT imaging, Computer Methods and Programs in Biomedicine 227 (2022) 107199.
L. Ma, H. Xue, G. Yang, Z. Zhang, C. Li, Y. Yao, Y. Teng, Scrdn: Residual dense network with self-calibrated convolutions for low dose CT image denoising, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 1045 (2023) 167625.
H. Liu, X. **, L. Liu, Low-dose CT image denoising based on improved dd-net and local filtered mechanism, Computational Intelligence and Neuroscience 2022 (2022).
N. T. Trung, D.-H. Trinh, N. L. Trung, M. Luong, Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields, Signal, Image and Video Processing (2022) 1–9.
Z. Li, W. Shi, Q. **ng, Y. Miao, W. He, H. Yang, Z. Jiang, Low-dose CT image denoising with improving wgan and hybrid loss function, Computational and Mathematical Methods in Medicine 2021 (2021).
Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, G. Wang, Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss, IEEE Transactions on Medical Imaging 37 (6) (2018) 1348–1357.
L. Marcos, J. Alirezaie, P. Babyn, Low dose CT image denoising using boosting attention fusion gan with perceptual loss, in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2021, pp. 3407–3410.
T. Michaeli, M. Irani, Nonparametric blind super-resolution, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 945–952.
S. Bell-Kligler, A. Shocher, M. Irani, Blind super-resolution kernel estimation using an internal-gan, Advances in Neural Information Processing Systems 32 (2019).
J. Gu, H. Lu, W. Zuo, C. Dong, Blind super-resolution with iterative kernel correction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1604–1613.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, Advances in Neural Information Processing Systems 30 (2017).
D. Hendrycks, K. Gimpel, Gaussian error linear units (gelus), ar**v preprint ar**v:1606.08415 (2016).
H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, G. Wang, Low-dose CT with a residual encoder-decoder convolutional neural network, IEEE Transactions on Medical Imaging 36 (12) (2017) 2524–2535.
X. Yin, Q. Zhao, J. Liu, W. Yang, J. Yang, G. Quan, Y. Chen, H. Shu, L. Luo, J.-L. Coatrieux, Domain progressive 3d residual convolution network to improve low-dose CT imaging, IEEE Transactions on Medical Imaging 38 (12) (2019) 2903–2913.
Z. Zhang, X. Liang, X. Dong, Y. **e, G. Cao, A sparse-view CT reconstruction method based on combination of densenet and deconvolution, IEEE Transactions on Medical Imaging 37 (6) (2018) 1407–1417.
D. Wu, K. Kim, G. El Fakhri, Q. Li, Iterative low-dose CT reconstruction with priors trained by artificial neural network, IEEE Transactions on Medical Imaging 36 (12) (2017) 2479–2486.
C. K. Ahn, H. **, C. Heo, J. H. Kim, Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac cta, in: Medical Imaging 2019: Physics of Medical Imaging, Vol. 10948, SPIE, 2019, pp. 1019–1024.
Z. A. Balogh, B. J. Kis, Comparison of CT noise reduction performances with deep learning-based, conventional, and combined denoising algorithms, Medical Engineering & Physics 109 (2022) 103897.
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2472–2481.
S. Anwar, N. Barnes, Real image denoising with feature attention, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 3155–3164.
Y. Cao, B. Liu, M. Long, J. Wang, Hashgan: Deep learning to hash with pair conditional wasserstein gan, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1287–1296.
X. Yi, P. Babyn, Sharpness-aware low-dose CT denoising using conditional generative adversarial network, Journal of Digital Imaging 31 (5) (2018) 655–669.
C. You, Q. Yang, H. Shan, L. Gjesteby, G. Li, S. Ju, Z. Zhang, Z. Zhao, Y. Zhang, W. Cong, et al., Structurally-sensitive multi-scale deep neural network for low-dose CT denoising, IEEE Access 6 (2018) 41839–41855.
W. Du, H. Chen, P. Liao, H. Yang, G. Wang, Y. Zhang, Visual attention network for low-dose ct, IEEE Signal Processing Letters 26 (8) (2019) 1152–1156.
R. Ge, G. Yang, C. Xu, Y. Chen, L. Luo, S. Li, Stereo-correlation and noise-distribution aware resvoxgan for dense slices reconstruction and noise reduction in thick low-dose ct, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 328–338.
K. Choi, M. Vania, S. Kim, Semi-supervised learning for low-dose CT image restoration with hierarchical deep generative adversarial network (hd-gan), in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019, pp. 2683–2686.
Y. Zhang, D. Hu, Q. Zhao, G. Quan, J. Liu, Q. Liu, Y. Zhang, G. Coatrieux, Y. Chen, H. Yu, Clear: comprehensive learning enabled adversarial reconstruction for subtle structure enhanced low-dose CT imaging, IEEE Transactions on Medical Imaging 40 (11) (2021) 3089–3101.
C. You, L. Yang, Y. Zhang, G. Wang, Low-dose CT via deep cnn with skip connection and network-in-network, in: Developments in X-Ray tomography XII, Vol. 11113, SPIE, 2019, pp. 429–434.
Z. Huang, J. Zhang, Y. Zhang, H. Shan, Du-gan: Generative adversarial networks with dual-domain u-net-based discriminators for low-dose CT denoising, IEEE Transactions on Instrumentation and Measurement 71 (2021) 1–12.
L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, S. Yan, Tokens-to-token vit: Training vision transformers from scratch on imagenet, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 558–567.
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. **ang, P. H. Torr, et al., Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 6881–6890.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012–10022.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations, 2020.
H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, W. Gao, Pre-trained image processing transformer, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 12299–12310.
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte, Swinir: Image restoration using swin transformer, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 1833–1844.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, Restormer: Efficient transformer for high-resolution image restoration, ar**v preprint ar**v:2111.09881 (2021).
K. He, H. Fan, Y. Wu, S. **e, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729–9738.
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, ar**v preprint ar**v:1409.1556 (2014).
L. A. Gatys, A. S. Ecker, M. Bethge, Image style transfer using convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2414–2423.
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, et al., The cancer imaging archive (tcia): maintaining and operating a public information repository, Journal of Digital Imaging 26 (6) (2013) 1045–1057.
M. Matsuki, T. Murakami, H. Juri, S. Yoshikawa, Y. Narumi, Impact of adaptive iterative dose reduction (aidr) 3d on low-dose abdominal CT: comparison with routine-dose CT using filtered back projection, Acta Radiologica 54 (8) (2013) 869–875.
S. Yamada, M. Axelsson, Y. Ishisaki, S. Konami, N. Takemura, R. L. Kelley, C. A. Kilbourne, M. A. Leutenegger, F. S. Porter, M. E. Eckart, et al., Poisson vs. gaussian statistics for sparse x-ray data: Application to the soft x-ray spectrometer, Publications of the Astronomical Society of Japan 71 (4) (2019) 75.
B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee, Enhanced deep residual networks for single image super-resolution, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 136–144.
D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, ar**v preprint ar**v:1412.6980 (2014).
A. Hore, D. Ziou, Image quality metrics: Psnr vs. ssim, in: 2010 20th International Conference on Pattern Recognition, IEEE, 2010, pp. 2366–2369.
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing 13 (4) (2004) 600–612.
Z. Qiao, C. Du, Rad-unet: a residual, attention-based, dense unet for CT sparse reconstruction, Journal of Digital Imaging 35 (6) (2022) 1748–1758.
J. Chi, C. Wu, X. Yu, P. Ji, H. Chu, Single low-dose CT image denoising using a generative adversarial network with modified u-net generator and multi-level discriminator, IEEE Access 8 (2020) 133470–133487.
Funding
This work was supported the National Natural Science Foundation of China under grant nos. U20A20197, 61973063, 61901098, and 61971118.
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Material preparation, data collection, and analysis were performed by Huan Wang, Jianning Chi, and Chengdong Wu. The first draft of the manuscript was written by Huan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Wang, H., Chi, J., Wu, C. et al. Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising. J Digit Imaging 36, 1894–1909 (2023). https://doi.org/10.1007/s10278-023-00831-y
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DOI: https://doi.org/10.1007/s10278-023-00831-y