Abstract
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects causing by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while changing the complexity of the network, minimally.
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Bencardino J T: Radiological society of north america (rsna) 2010 annual meeting. Skelet Radiol 40: 1109–1112, 2011
Donya M, Radford M, ElGuindy A, Firmin D, Yacoub M H (2015) Radiation in medicine: origins, risks and aspirations. Global Cardiology Science and Practice pp 57
Ehman E C, Yu L, Manduca A, Hara A K, Shiung M M, Jondal D, Lake D S, Paden R G, Blezek D J, Bruesewitz M R, et al: Methods for clinical evaluation of noise reduction techniques in abdominopelvic CT. Radiographics 34 (4): 849–862, 2014
Wang J, Lu H, Liang Z, Eremina D, Zhang G, Wang S, Chen J, Manzione J: An experimental study on the noise properties of x-ray CT sinogram data in radon space. Phys Med Biol 53 (12): 3327, 2008
Macovski A: Medical Imaging Systems, vol 20 NJ: Prentice-Hall Englewood Cliffs, 1983
Manduca A, Yu L, Trzasko J D, Khaylova N, Kofler J M, McCollough C M, Fletcher J G: Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys 36 (11): 4911–4919, 2009
Wang J, Li T, Lu H, Liang Z: Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography. IEEE Trans Med Imaging 25 (10): 1272–1283, 2006
Pickhardt P J, Lubner M G, Kim D H, Tang J, Ruma J A, del Rio A M, Chen G H: Abdominal CT with model-based iterative reconstruction (mbir): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. Am J Roentgenol 199 (6): 1266–1274, 2012
Fletcher J G, Grant K L, Fidler J L, Shiung M, Yu L, Wang J, Schmidt B, Allmendinger T, McCollough C H: Validation of dual-source single-tube reconstruction as a method to obtain half-dose images to evaluate radiation dose and noise reduction: phantom and human assessment using CT colonography and sinogram-affirmed iterative reconstruction (safire). J Comput Assist Tomogr 36 (5): 560–569, 2012
Aharon M, Elad M, Bruckstein A, et al.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11): 4311, 2006
Chen Y, Yin X, Shi L, Shu H, Luo L, Coatrieux J L, Toumoulin C: Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Phys Med Biol 58 (16): 5803, 2013
Abhari K, Marsousi M, Alirezaie J, Babyn P (2012) Computed tomography image denoising utilizing an efficient sparse coding algorithm. 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) pp 259–263
Buades A, Coll B, Morel J M (2005) A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 2, pp 60–65. IEEE
Chen Y, Yang Z, Hu Y, Yang G, Zhu Y, Li Y, Chen W, Toumoulin C, et al.: Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means. Phys Med Biol 57 (9): 2667, 2012
Dabov K, Foi A, Katkovnik V, Egiazarian K: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Signal Process 16 (8): 2080–2095, 2007
Hashemi S, Paul N S, Beheshti S, Cobbold R S (2015) Adaptively tuned iterative low dose CT image denoising. Computational and mathematical methods in medicine pp 2015
Kang D, Slomka P, Nakazato R, Woo J, Berman D S, Kuo C C J, Dey D: Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3d algorithm.. In: Medical imaging 2013: Image processing, vol. 8669, p. 86692g. International society for optics and photonics, 2013
Ioffe S, Szegedy C: Batch normalization: accelerating deep network training by reducing internal covariate shift.. In: ICML, 2015
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp 770–778
Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G: Low-dose CT via convolutional neural network. Biomed Opt Express 8(2): 679–694, 2017
Dong C, Loy C C, He K, Tang X: Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2): 295–307, 2016
Nishio M, Nagashima C, Hirabayashi S, Ohnishi A, Sasaki K, Sagawa T, Hamada M, Yamashita T: Convolutional auto-encoder for image denoising of ultra-low-dose CT. Heliyon 3 (8): e00,393, 2017
Chen H, Zhang Y, Kalra M K, Lin F, Chen Y, Liao P, Zhou J, Wang G: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging 36 (12): 2524–2535, 2017
Kang E, Min J, Ye J C (2017) A deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction. Medical physics 44(10)
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A C, Bengio Y (2014) Generative adversarial networks. arXiv:1406.2661
Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. arXiv:1605.05396
Ledig C, Theis L, Huszár F., Caballero J, Cunningham A, Acosta A, Aitken A P, Tejani A, Totz J, Wang Z, et al: Photo-realistic single image super-resolution using a generative adversarial network.. In: CVPR, vol 2, p 4, 2017
Vondrick C, Pirsiavash H, Torralba A: Generating videos with scene dynamics.. In: Advances in neural information processing systems, pp 613–621, 2016
Yi X, Babyn P (2018) Sharpness-aware low-dose CT denoising using conditional generative adversarial network. Journal of digital imaging, pp 1–15
Wolterink J M, Leiner T, Viergever M A, Išgum I.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 36 (12): 2536–2545, 2017
Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra M K, Zhang Y, Sun L, Wang G (2018) Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE transactions on medical imaging
Yang Q, Yan P, Kalra M K, Wang G (2017) CT image denoising with perceptive deep neural networks. arXiv:1702.07019
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Bevins N, Szczykutowicz T, Supanich M: Tu-c-103-06: a simple method for simulating reduced-dose images for evaluation of clinical CT protocols. Med Phys 40 (6Part26): 437–437, 2013
Zeng D, Huang J, Bian Z, Niu S, Zhang H, Feng Q, Liang Z, Ma J: A simple low-dose x-ray CT simulation from high-dose scan. IEEE Trans Nucl Sci 62 (5): 2226–2233, 2015
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122
Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40 (4): 834–848, 2018
Mao X, Shen C, Yang Y B: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections.. In: Advances in neural information processing systems, pp 2802–2810, 2016
Wang T, Sun M, Hu K: Dilated deep residual network for image denoising.. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI), pp 1272–1279. IEEE, 2017
Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing
Zhang K, Zuo W, Gu S, Zhang L: Learning deep cnn denoiser prior for image restoration.. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2017
Huang G, Liu Z, Weinberger KQ, van der Maaten L (2016) Densely connected convolutional networks. arXiv:1608.06993
Sobel I (1990) An isotropic 3× 3 image gradient operator. Machine vision for three-dimensional scenes pp 376–379
Johnson J, Alahi A, Fei-Fei L: Perceptual losses for real-time style transfer and super-resolution.. In: European Conference on Computer Vision, pp 694–711. Springer, 2016
Deng J, Dong W, Socher R, Li L J, Li K, Fei-Fei L: Imagenet: a large-scale hierarchical image database.. In: CVPR09, 2009
Lingle W, Erickson B, Zuley M, Jarosz R, Bonaccio E, Filippini J, Gruszauskas N (2016) Radiology data from the cancer genome atlas breast invasive carcinoma [tcga-brca] collection. The Cancer Imaging Archive
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, et al: The cancer imaging archive (tcia): maintaining and operating a public information repository. J Digit Imaging 26 (6): 1045–1057, 2013
Yi X (2019) Recent publication. http://homepage.usask.ca/xiy525/
Gavrielides MA, Kinnard LM, Myers KJ, Peregoy J, Pritchard WF, Zeng R, Esparza J, Karanian J, Petrick N: A resource for the assessment of lung nodule size estimation methods: database of thoracic ct scans of an anthropomorphic phantom. Opt Express 18 (14): 15,244–15,255, 2010. https://doi.org/10.1364/OE.18.015244. http://www.opticsexpress.org/abstract.cfm?URI=oe-18-14-15244
Glorot X, Bengio Y: Understanding the difficulty of training deep feedforward neural networks.. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp 249–256, 2010
Acknowledgements
This work was supported in part by a research grant from Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to thank Dr. Paul Babyn and Troy Anderson for the acquisition of the piglet dataset. The results shown here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.
Funding
This work was supported in part by a research grant from Natural Sciences and Engineering Research Council of Canada (NSERC).
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Gholizadeh-Ansari, M., Alirezaie, J. & Babyn, P. Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer. J Digit Imaging 33, 504–515 (2020). https://doi.org/10.1007/s10278-019-00274-4
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DOI: https://doi.org/10.1007/s10278-019-00274-4