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http://dx.doi.org/10.7742/jksr.2020.14.7.937

Evaluation of Machine Learning Methods to Reduce Stripe Artifacts in the Phase Contrast Image due to Line-Integration Process  

Kim, Myungkeun (School of Mechanical Engineering, Pusan National University)
Oh, Ohsung (School of Mechanical Engineering, Pusan National University)
Lee, Seho (School of Mechanical Engineering, Pusan National University)
Lee, Seung Wook (School of Mechanical Engineering, Pusan National University)
Publication Information
Journal of the Korean Society of Radiology / v.14, no.7, 2020 , pp. 937-946 More about this Journal
Abstract
The grating interferometer provides the differential phase contrast image of an phase object due to refraction of the wavefront by the object, and it needs to be converted to the phase contrast image. The line-integration process to obtain the phase contrast image from a differential phase contrast image accumulates noise and generate stripe artifacts. The stripe artifacts have noise and distortion increases to the integration direction in the line-integrated phase contrast image. In this study, we have configured and compared several machine learning methods to reduce the artifacts. The machine learning methods have been applied to simulated numerical phantoms as well as experimental data from the X-ray and neutron grating interferometer for comparison. As a result, the combination of the wavelet preprocessing and machine learning method (WCNN) has shown to be the most effective.
Keywords
Phase Contrast; X-ray; Neutron; Radiography; Artifacts; Machine Learning; Imaging; Wavelet;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 K. Zhang, W. Zuo, Y. Chen, "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising", Transaction on Image Processing, Vol. 26, No. 7, pp. 3142-3155, 2017. http://dx.doi.org/10.1109/TIP.2017.2662206   DOI
2 X. Kuang, X. Sui, "Single Infrared Image Stripe Noise Removal Using Deep Convolutional Networks", IEEE Photonics Journal, Vol. 9, No. 4, pp. 1-13, 2017. http://dx.doi.org/10.1109/JPHOT.2017.2717948   DOI
3 S. Lee, O. Oh, Y. Kim, I. Kim, J. Kim, S. W. Lee, "The system design and characteristics of the 54.3 keV Talbot-Lau grating interferometry for industrial applications", Journal of the Korean Physical Society, Vol. 73, No. 12, pp. 1827-1833, 2018. https://doi.org/10.3938/jkps.73.1827   DOI
4 Youngju Kim, Jongyul Kim, Daeseung Kim, Daniel S. Hussey, Seung Wook Lee, "Characterization of the phase sensitivity, visibility, and resolution in a symmetric neutron grating interferometer", Review of Scientific Instruments vol. 90, 073704, 2019. http://dx.doi.org/10.1063/1.5089588   DOI
5 I. Zanette, T. Zhou, A. Burvall, "Speckle-Based X-Ray Phase-Contrast and Dark-Field Imaging with a Laboratory Source", Physical Review letters, Vol. 112, pp. 253903, 2014. http://dx.doi.org/10.1103/PhysRevLett.112.253903   DOI
6 Y. Wang, W. Huang, Q. He, Z. Zhu, "Adaptive weighted total variation regularized phase retrieval in differential phase-contrast imaging", Optical Engineering, Vol. 57, No. 5, pp. 53108, 2018. http://dx.doi.org/10.1117/1.OE.57.5.053108   DOI
7 M. N Wernick, Y. Yang, I. Mondal, "Computation of mass-density images from x-ray refraction-angle images", Physics in Medicine & Biology, Vol. 51, pp. 1769-1778, 2006. http://dx.doi.org/10.1088/0031-9155/51/7/009   DOI
8 J. Matias, D. Martino, J. L. Flores, F. Pfeiffer, "Phase retrieval from one partial derivative", Optics Letters, Vol. 38, No. 22, pp. 4813-4819, 2013. http://dx.doi.org/10.1364/OL.38.004813   DOI
9 B. Munch, P. Trtik, F. Marone, "Stripe and ring artifact removal with combined wavelet - Fourier filtering", Optics Express, Vol. 17, No. 10, pp. 8567-8591, 2009. http://dx.doi.org/10.1364/OE.17.008567   DOI
10 Z. He, Y. Cao, Y. Dong, J. Yang, "Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: a deep-learning approach", Applied Optics, Vol. 57 No. 18, pp. 155, 2018. http://dx.doi.org/10.1364/AO.57.00D155   DOI
11 P. Xiao, Y. Guo, P. Zhuang, "Removing Stripe Noise From Infrared Cloud Images via Deep Convolutional Networks", IEEE Photonics Journal, Vol. 10, No. 4, pp. 1-14, 2018. http://dx.doi.org/10.1109/JPHOT.2018.2854303   DOI
12 L. Xu, J. SJ Ren, C. Liu, J. Jia, "Deep convolutional neural network for image deconvolution", Advanced in Neural Information Processing Systems Vol. 27, pp. 1790-1798, 2014.
13 M. R. Arnison, K. G. Larkin, C. J. R. Sheppard, "Linear phase imaging using differential interference contrast microscopy", Journal of Microscopy, Vol. 214, pp. 7-12, 2003. http://dx.doi.org/10.1111/j.0022-2720.2004.01293.x   DOI
14 R. A. Peters II, "A New Algorithm for Image Noise Reduction using Mathematical Morphology", IEEE Tranactions on Image Processing, Vol. 4, No. 3, pp. 554-568, 1995. http://dx.doi.org/10.1109/83.382491   DOI
15 R. Hain, C. J. Kahler, C. Tropea, "Comparison of CCD, CMOS and intensified camera", Experiments in Fluids, Vol. 42, No. 3, pp. 403-411, 2007. http://dx.doi.org/10.1007/s00348-006-0247-1   DOI
16 Y. Zhu, C. Huang, "An Improved Median Filtering Algorithm for Image Noise Reduction", Physcis Procedia, Vol. 25, pp. 609-616, 2012. http://dx.doi.org/10.1016/j.phpro.2012.03.133   DOI
17 C. Kottler, C. David, "A two-directional approach for grating based differential phase contrast imaging using hard x-rays", Optics Express Vol. 15, No. 3, pp. 1175-1181, 2007. http://dx.doi.org/10.1364/OE.15.001175   DOI