Deep-learning-based denoising of X-ray differential phase and dark-field images.

Journal: European journal of radiology

Published Date: Apr 11, 2023

Abstract

PURPOSE: Statistical photon noise has always been a common problem in X-ray multi-contrast imaging and significantly influenced the quality of retrieved differential phase and dark-field images. We intend to develop a deep learning-based denoising algorithm to reduce the noise of retrieved X-ray differential phase and dark-field images.

Authors

Kun Ren

School of Microelectronics, Hefei University of Technology, Hefei 230009, China.
Yao Gu

School of Physics, Hefei University of Technology, Hefei 230009, China.
Mengsi Luo

School of Physics, Hefei University of Technology, Hefei 230009, China.
Heng Chen

Medical College, Guizhou University, Jiaxiu Road, Huaxi Zone, Guiyang 550025, P. R. China.
Zhili Wang

School of Physics, Hefei University of Technology, Hefei 230009, China. Electronic address: dywangzl@hfut.edu.cn.

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Radiography Signal-To-Noise Ratio Tomography, X-Ray Computed X-Rays

External Resources

View on PubMed Access via DOI PubMed (37098281)

Deep-learning-based denoising of X-ray differential phase and dark-field images.

Abstract

Authors

Keywords

External Resources

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