基于生成对抗网络的低剂量能谱层析成像去噪算法

doi:10.13229/j.cnki.jdxbgxb20190554

摘要/Abstract

摘要：

为提升在低辐射剂量条件下能谱式计算机断层扫描(CT)的重建图像质量，提出了一种基于复合感知损失函数的生成对抗网络去噪模型。此方法将像素空间与人类感知的特征空间结合到网络的生成对抗过程，并引入残差学习解决网络层数加深导致的图像细节丢失问题。通过采用多个能段的CT图像作为输入，同时利用了能段内的空间相关性和能段间的能量相关性，提高了能谱CT图像的视觉灵敏度。实验结果表明:该方法将峰值信噪比提高约5 dB，结构相似性指数提高约0.2，特征相似性指数提高约0.06。与当前的低剂量CT影像去噪算法相比，本文模型可实现更好的噪声去除效果，同时能保留诊断必要的细节信息，显著提高了低剂量能谱CT图像的质量。

关键词: 信息处理技术, 低剂量, 生成对抗网络, 残差学习

Abstract:

The continuous development and widespread use of computed tomography(CT) in medical practice have caused patients and doctors’ attention to the risk of cancer due to radiation dose. However, spectral CT can generate severe Poisson noise as a result of the division of multiple energy bins while reducing the radiation dose, which has an adverse effect on clinical diagnosis. Aiming at improving the reconstructed image quality of spectral CT under low radiation dose, a denoising model based on a generative adversarial network with the hybrid perceptual loss was proposed. The method combines pixel space with the human-perceived feature space into generative adversarial network, and introduces the residual learning to reuse features from different levels to avoid the loss of important details that come from the increase in the number of network layers. Then the Wasserstein distance is chosen instead of Jensen-Shannon to improve the stability of training. The inputs are from multiple energy bins, so that the spatial correlation of the reconstructed image from a single energy bin and the energy correlation between different reconstructed images from multiple energy bins can be utilized to improve the visual sensitivity of low dose spectral CT images. The experiments were performed on the simulated phantom data sets from Duke University Medical Center. The results show that the proposed method increases the PSNR by about 5 dB, the structure similarity (SSIM) value by about 0.2, and the feature similarity (FSIM) value about by 0.06. Compared with current advanced low-dose CT denoising method, the hybrid loss function based generative and adversarial network achieves a better noise removal, while retaining the necessary details for diagnosis. Moreover, the spectral information also plays a positive role in the recovery of noise images, and the use of hybrid perceptual loss is superior to any single loss. Overall, the proposed generative adversarial network with a hybrid loss method can significantly improve the quality of low-dose spectral CT images.

Key words: information processing technology, low dose, generative adversarial network, residual learning

中图分类号:

TP391.4

史再峰,李金卓,曹清洁,李慧龙,胡起星. 基于生成对抗网络的低剂量能谱层析成像去噪算法[J]. 吉林大学学报(工学版), 2020, 50(5): 1755-1764.

Zai-feng SHI,Jin-zhuo LI,Qing-jie CAO,Hui-long LI,Qi-xing HU. Low⁃dose spectral computer⁃tomography imaging denoising method via a generative adversarial network[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1755-1764.

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参考文献 14

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能量区间	指标	MSE 损失	纹理损失	感知损失	复合感知损失
30~80 keV	PSNR/dB	37.39	35.65	34.80	37.41
	SSIM	0.8169	0.8467	0.7912	0.8919
	FSIM	0.9712	0.9633	0.9485	0.9738
80~120 keV	PSNR/dB	39.24	37.41	36.61	38.70
	SSIM	0.9236	0.9104	0.9088	0.9255
	FSIM	0.9738	0.9691	0.9559	0.9774