School of Microelectronics, Hefei University of Technology, Hefei 230009, China.
School of Physics, Hefei University of Technology, Hefei 230009, China.
Eur J Radiol. 2023 Jun;163:110835. doi: 10.1016/j.ejrad.2023.110835. Epub 2023 Apr 11.
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.
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.
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.
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.
统计光子噪声一直是 X 射线多对比度成像中的一个常见问题，极大地影响了所提取的差分相位和暗场图像的质量。我们旨在开发一种基于深度学习的去噪算法，以降低所提取的 X 射线差分相位和暗场图像的噪声。
提出了一种基于深度学习的图像噪声抑制算法（命名为 DnCNN-P）。我们提出了两种不同的去噪模式：重建去噪模式（R-D 模式）和去噪重建模式（D-R 模式）。R-D 模式对重建图像进行去噪，而 D-R 模式对原始相移数据进行去噪。在不同的光子计数和可见度下评估了这两种去噪模式。
实验结果表明，使用算法 DnCNN-P，在不同的实验条件下，D-R 模式始终表现出更好的降噪效果，即使在光子计数低和/或可见度低的情况下也是如此。在检测到的光子计数为 1800 和可见度为 0.3 的情况下，与未去噪的差分相位图像相比，D-R 和 R-D 模式的标准差分别降低了 89.1%和 16.4%。与未去噪的暗场图像相比，D-R 和 R-D 模式的标准差分别降低了 83.7%和 12.6%。
新型监督 DnCNN-P 算法可以显著降低所提取的 X 射线差分相位和暗场图像的噪声。我们相信，这种新算法可以成为提高 X 射线差分相位和暗场图像质量的一种有前途的方法，从而提高未来生物医学应用中的剂量效率。
