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KMID : 1151820200140070937
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Journal of the Korean Society of Radiology
2020 Volume.14 No. 7 p.937 ~ p.946
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Evaluation of Machine Learning Methods to Reduce Stripe Artifacts in the Phase Contrast Image due to Line-Integration Process
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Kim Myung-Keun
Oh Oh-Sung
Lee Se-Ho
Lee Seung-Wook
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Abstract
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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.
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KEYWORD
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Phase Contrast, X-ray, Neutron, Radiography, Artifacts, Machine Learning, Imaging, Wavelet
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