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Proceedings Paper

Novel learning-based Moiré artifacts reduction method in x-ray Talbot-Lau differential phase contrast imaging
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Paper Abstract

In this work, we present a novel convolutional neural network (CNN) enabled Moiré artifacts reduction framework for the three contrast mechanism images, i.e., the absorption image, the differential phase contrast (DPC) image, and the dark-field (DF) image, obtained from an x-ray Talbot-Lau phase contrast imaging system. By mathematically model the various potential non-ideal factors that may cause Moiré artifacts as a random fluctuation of the phase stepping position, rigorous theoretical analyses show that the Moiré artifacts on absorption images may have similar distribution frequency as of the detected phase stepping Moiré diffraction fringes, whereas, their periods on DPC and DF images may be doubled. Upon these theoretical findings, training dataset for the three different contrast mechanisms are synthesized properly using natural images. Afterwards, the three datasets are trained independently by the same modified auto-encoder type CNN. Both numerical simulations and experimental studies are performed to validate the performance of this newly developed Moiré artifacts reduction method. Results show that the CNN is able to reduce residual Moiré artifacts efficiently. With the improved signal accuracy, as a result, the radiation dose efficiency of the Talbot-Lau interferometry imaging system can be greatly enhanced.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109484S (1 March 2019); doi: 10.1117/12.2506455
Show Author Affiliations
Jianwei Chen, Shenzhen Institutes of Advanced Technology (China)
Jiongtao Zhu, Shenzhen Institutes of Advanced Technology (China)
Wuhan Univ. of Technology (China)
Qiyang Zhang, Shenzhen Institutes of Advanced Technology (China)
Dong Liang, Shenzhen Institutes of Advanced Technology (China)
Wuhan Univ. of Technology (China)
Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)
Wuhan Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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