Low-Dose CT Denoising Algorithm Based on Improved Cycle GAN

Siqi Zhu; Jue Wang; Yufang Cai

doi:10.3788/AOS202040.2210002

[1] Liu J, Kang Y Q, Gu Y B et al. Low dose computed tomography image reconstruction based on sparse tensor constraint[J]. Acta Optica Sinica, 39, 0811004(2019).

[2] Sigal-Cinqualbre A B, Hennequin R, Abada H T et al. Low-kilovoltage multi-detector row chest CT in adults: feasibility and effect on image quality and iodine dose[J]. Radiology, 231, 169-174(2004).

[3] Yang W, Zhang H J, Yang J et al. Improving low-dose CT image using residual convolutional network[J]. IEEE Access, 5, 24698-24705(2017).

[4] Wang J, Li T F, Lu H B et al. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography[J]. IEEE Transactions on Medical Imaging, 25, 1272-1283(2006).

[5] Whiting B R. Signal statistics in X-ray computed tomography[J]. Proceedings of SPIE, 4682, 53-60(2002).

[6] Liu Y, Ma J H, Fan Y et al. Adaptive-weighted total variation minimization for sparse data toward low-dose X-ray computed tomography image reconstruction[J]. Physics in Medicine and Biology, 57, 7923-7956(2012).

[7] Chen Y, Yin X D, Shi L Y et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing[J]. Physics in Medicine and Biology, 58, 5803-5820(2013).

[8] Shi Z F, Li J Z, Cao Q J, Technology Edition et al. -09-02)[2020-06-10](2019). https://doi.org/10.13229/j.cnki.jdxbgxb0554.

[9] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

[10] Qin R. Edge information extraction algorithm for CT cerebrovascular medical image based on neural network[J]. Journal of Electronic Measurement and Instrument, 24, 346-352(2010).

[11] Zhang X N, Zhong X, Zhu R F et al. Scene classification of remote sensing images based onIntegrated convolutional neural networks[J]. Acta Optica Sinica, 38, 1128001(2018).

[12] Zhang Y G, Yi B S, Wu C Y et al. Low-dose CT image denoising method based on convolutional neural network[J]. Acta Optica Sinica, 38, 0410003(2018).

[13] Chen H, Zhang Y, Zhang W H et al. Low-dose CT denoising with convolutional neural network[C]∥2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), April 18-21, 2017, Melbourne, VIC, Australia., 143-146(2017).

[14] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

[15] Chen H, Zhang Y, Kalra M K et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Transactions on Medical Imaging, 36, 2524-2535(2017).

[16] Wolterink J M, Leiner T, Viergever M A et al. Generative adversarial networks for noise reduction in low-dose CT[J]. IEEE Transactions on Medical Imaging, 36, 2536-2545(2017).

[17] Yang Q S, Yan P K, Zhang Y B et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss[J]. IEEE Transactions on Medical Imaging, 37, 1348-1357(2018).

[18] Xu Z C, Ye C, Du Z L et al. Low-dose CT image denoising method based on WGAN-gp[J]. Optics & Optoelectronic Technology, 17, 101-107(2019).

[19] Wang T H, Lei Y, Tian Z et al. Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy[J]. Journal of Medical Imaging, 6, 043504(2019).

[20] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2242-2251(2017).

[21] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

[22] Fu L, Lee T C, Kim S M et al. Comparison between pre-log and post-log statistical models in ultra-low-dose CT reconstruction[J]. IEEE Transactions on Medical Imaging, 36, 707-720(2017).

[23] Goodfellow I J, Pouget-Abadie J, Mirza M et al. -06-10) [2020-06-10], org/abs/1406, 2661(2014). https://arxiv.

[24] Tzeng E, Hoffman J, Saenko K et al. Adversarial discriminative domain adaptation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2962-2971(2017).

[25] Li C Y, Liu H, Chen C Y et al. -11-05)[2020-06-10], org/abs/1709, 01215(2017). https://arxiv.

[26] Guan Y Q, Yan Q R, Yang S T et al. Single-photon compressive imaging based on residual codec network[J]. Acta Optica Sinica, 40, 0111022(2020).

[27] Zeng D, Huang J, Bian Z Y et al. A simple low-dose X-ray CT simulation from high-dose scan[J]. IEEE Transactions on Nuclear Science, 62, 2226-2233(2015).

[28] Cai Y F, Chen T Y, Wang J et al. Image noise reduction in computed tomography with non-local MeansAlgorithm based on adaptive filtering coefficients[J]. Acta Optica Sinica, 40, 0710001(2020).

[29] Agatston A S, Janowitz W R, Hildner F J et al. Quantification of coronary artery calcium using ultrafast computed tomography[J]. Journal of the American College of Cardiology, 15, 827-832(1990).