Deep-learning-based denoising of X-ray differential phase and dark-field images. Issue 163 (June 2023)

• Record Type: Journal Article
• Authors: Ren, Kun; Gu, Yao; Luo, Mengsi; Chen, Heng; Wang, Zhili
• Is Part Of: European journal of radiology. Issue 163(2023)
• Publication Date: 2023-06
• Subjects: X-ray imaging -- Deep learning -- Image denoising -- Dark-field image; SNR Signal-to-noise ratio -- CNN Convolutional Neural Network -- PS Phase stepping -- PSNR Peak signal-to-noise ratio -- SSIM Structural similarity -- ROI Regions of interest -- R-D Retrieval-Denoising -- D-R Denoising- Retrieval; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2023.110835
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Deep learning based denoising algorithm for X-ray multi-contrast images. Standard deviation reduced by 89.1% for denoised differential phase images. Standard deviation reduced by 83.7% for denoised dark-field images. Denoising raw intensity images is recommended before phase retrieval. 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. Methods: A novel deep learning based image noise suppression algorithm (named DnCNN-P) is presented. We proposed two different denoising modes: Retrieval-Denoising mode (R-D mode) and Denoising-Retrieval mode (D-R mode). While the R-D mode denoises the retrieved images, the D-R mode denoises the raw phase stepping data. The two denoising modes are evaluated under different photon counts and visibilities. Results: Experimental results show that with the algorithm DnCNN-P used, the D-R mode always exhibits a better noise reduction under diverse experimental conditions, even in the case of a low photon count and/or a low visibility. With a detected photon count of 1800 and a visibility of 0.3, compared to the differential phase images without denoising, the standard deviation is reduced by 89.1% and 16.4% in the D-R and R-D modes. Compared to the dark-field images without denoising, the standard deviation is reduced by 83.7% and 12.6% in the D-R and R-D modes, respectively. Conclusions. The novel supervised DnCNN-P algorithm can significantly reduce the noise in retrieved X-ray differential phase and dark-field images. We believe this novel algorithm can be a promising approach to improve the quality of X-ray differential phase and dark-field images, and therefore dose efficiency in future biomedical applications.