Low dose x-ray CT image denoising via U-net in projection domain

Xiaofu Song; Linlin Zhu; Xiaoqi Xi; Yu Han; Lei Li; Zhiwei Feng; Mingwan Zhu; Guanyu Kang; Bin Yan

doi:10.1117/12.2580369

5 November 2020 Low dose x-ray CT image denoising via U-net in projection domain

Xiaofu Song, Linlin Zhu, Xiaoqi Xi, Yu Han, Lei Li, Zhiwei Feng, Mingwan Zhu, Guanyu Kang, Bin Yan

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 1156513 (2020) https://doi.org/10.1117/12.2580369
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Low dose computed tomography (LDCT) has attracted considerable interest in medical imaging fields. Reducing tube current intensity and decrease the exposure time are the two main ways in clinic applications. Nevertheless, the resulting statistical noise will seriously degrade CT image quality for diagnosis. To make full use of the original projection data as well as further improving the small dataset processing ability of U-net, this study aimed to investigate a low dose X-ray CT image denoising method via U-net in projection domain (PDI U-net). Meanwhile, in view of avoiding the excessive smoothing of the small structures, the inception module is introduced in the encoding stage of the network. And different convolution kernel operations of 1×1, 3×3, and 5×5 are used in parallel to obtain multi-scale image features, increasing the depth and width of the network while reducing the parameters. Furthermore, the shortcut connection is utilized to transfer the low-level area local detail information to the high-level area. By merging it with the global information of the high-level area, the proposed network can maintain the image details while de-noising. The experimental results show that the method proposed can significantly improve the image quality with clear feature edges and close visual appearance to the reference high dose CT images. Compared with LDCT and Residual Encoder-Decoder Convolutional Neural Network (RED-CNN), the peak signal to noise radio (PSNR) is improved by 9.02dB and 2.74dB, and the structural similarity (SSIM) is improved by 0.43 and 0.07, respectively.

Citation Download Citation

Xiaofu Song, Linlin Zhu, Xiaoqi Xi, Yu Han, Lei Li, Zhiwei Feng, Mingwan Zhu, Guanyu Kang, and Bin Yan "Low dose x-ray CT image denoising via U-net in projection domain", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 1156513 (5 November 2020); https://doi.org/10.1117/12.2580369

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KEYWORDS

X-ray computed tomography

Convolution

Image quality

Computed tomography

Computer programming

Image processing

Image denoising

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