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http://dx.doi.org/10.7742/jksr.2020.14.7.991

Development of Convolutional Network-based Denoising Technique using Deep Reinforcement Learning in Computed Tomography  

Cho, Jenonghyo (Department of Radiological Science, Konyang University)
Yim, Dobin (Department of Medical Science, Konyang University)
Nam, Kibok (Department of Radiological Science, Konyang University)
Lee, Dahye (Department of Radiological Science, Konyang University)
Lee, Seungwan (Department of Radiological Science, Konyang University)
Publication Information
Journal of the Korean Society of Radiology / v.14, no.7, 2020 , pp. 991-1001 More about this Journal
Abstract
Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.
Keywords
Deep reinforcement learning; Denoising; Computed tomography; Image quality;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 D. J. Brenner, E. J. Hall, "Computed tomography-an increasing source of radiation exposure", New England Journal of Medicine, Vol. 357, No. 22, pp. 2277-2284, 2007. http://dx.doi.org/10.1056/NEJMra072149   DOI
2 S. Leng, L. Yu, J. Wang, J. G. Fletcher, C. A. Mistretta, C. H. Mc Collough, "Noise reduction in spectral CT : Reducing dose and breaking the trade-off between image noise and energy bin selection", The International Journal of Medical Physics Research and Practice, Vol. 38, No. 9, pp. 4946-4957, 2011. http://dx.doi.org/10.1118/1.3609097   DOI
3 B. Li, G. B. Avinash, J. Hsieh, "Resolution and noise trade-off analysis for volumetric CT", The International Journal of Medical Physics Research and Practice, Vol. 34, No. 10, pp. 3732-3738, 2007. https://doi.org/10.1118/1.2779128   DOI
4 J. B. Seo, N. Kim, "Deep Learning in Medical Imaging: General Overview", Korean Journal of Radiology, Vol. 18, No. 4, pp. 570-584, 2017. http://dx.doi.org/10.3348/kjr.2017.18.4.570   DOI
5 K. Sung Jun, "Deep Network for Detail Enhancement in Image Denoising", Journal of Korea Multimedia Society, Vol. 22, No. 6, pp 646-654. 2019.   DOI
6 Y. L. Cun, K. Kavukcuoglu, C. Farabet, "Convolutional networks and applications in vision", Proceedings of 2010 IEEE International Symposium on Cricuits and Systems, pp. 253-256, 2010.
7 R. Furuta, N. Inoue, T. Yamasaki, "PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing", IEEE Transactions on Multimedia, Vol. 22, No. 7, pp 1704-1719, 2020.   DOI
8 F. Navarro, A. Sekuboyina, D. Waldmannstetter, J. C. Peeken, S. E. Combs, B. H. Menze, "Deep Reinforcement Learning for Organ Localization in CT", PMLR 121:544-554, 2020.
9 V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller, "Playing Atari with Deep Reinforcement Learning", NIPS Deep Learining Workshop 2013.
10 I. Ali, G. R. Hart, G. Gunabushanam, et al., "Lung Nodule Detection via Deep Reinforcement Learning", Frontiers in Oncology, Vol. 8, pp. 1-7, 2018. http://dx.doi.org/10.3389/fonc.2018.00108   DOI
11 The Cancer Imaging Archive (TCIA) Public Access, SPIE-AAPM Lung CT Challenge, Calibration Set.
12 The Cancer Imaging Archive (TCIA) collections, http://www.cancerimagingarchive.net/
13 K. Nam, J. Cho, S. Lee, B. Kim, D. Yim, D. Lee, "Image Quality Evaluation in Computed Tomography Using Super-resolution Convolutional Neural Network", Journal of the Korean Society of Radiology, Vol. 14, No. 3, pp. 211-219, 2020. https://doi.org/10.7742/jksr.2020.14.3.211   DOI
14 E. Shelhamer, J. Long, T. Darrell, "Fully Convolutional Networks for Semantic Segmentation", IEEE, Vol. 39, No. 4, pp. 640-651, 2016. https://doi.org/10.1109/tpami.2016.2572683   DOI
15 C. Shen, Y. Gonzalez, L. Chen, S. B. Jiang, X. Jia, "Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning", IEEE Transactions on Medical Imaging, Vol. 37, No. 6, pp. 1430-1439, 2018. http://dx.doi.org/10.1109/TMI.2018.2823679   DOI
16 K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation", EMNLP, pp. 1724-1734, 2014.
17 H. Sak, AW. Senior, F. Beaufays, "Long short-term memory recurrent neural network architectures for large scale acoustic modeling", INTERSPEECH 2014.
18 D. Kingma, J. Ba, "Adam: A method for stochastic optimization", International Conference on Learning Representations(ICLR), 2015.
19 J. Bergstra, Y. Bengio, "Random Search for Hyper-Parameter Optimization", Journal of Machine Learning Research, Vol. 13, pp. 281-305, 2012.
20 A. Hore, D. Ziou, "Image Quality Metrics: PSNR vs. SSIM", 2010 20th International Conference on Pattern Recognition, 2010.
21 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, Vol. 13, No. 4, pp. 600-612, 2004. http://dx.doi.org/10.1109/TIP.2003.819861   DOI
22 Y. J. You, "Correlation with covariance", Gosigye, Vol. 42, No. 3, pp 423-425.