南方医科大学学报

南方医科大学学报 ›› 2022, Vol. 42 ›› Issue (5): 724-732.doi: 10.12122/j.issn.1673-4254.2022.05.14

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基于非局部能谱相似特征的基物质分解方法用于双能CT图像去噪

王 蕾，王永波，边兆英，马建华，黄 静   

  1. 南方医科大学生物医学工程学院，广东 广州 510515；广州市医用放射成像与检测技术重点实验室，广东 广州 510515
  • 出版日期:2022-05-20 发布日期:2022-06-02

A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images

WANG Lei, WANG Yongbo, BIAN Zhaoying, MA Jianhua, HUANG Jing   

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
  • Online:2022-05-20 Published:2022-06-02

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摘要： 目的 提出一种非局部能谱相似特征引导的双能CT基物质分解方法（NSSD-Net）以抑制低剂量能谱CT基物质图像的相关性噪声。方法 首先构建模型驱动的双能CT迭代分解模型，采用迭代软阈值算法（ISTA）优化分解模型目标函数的求解过程，并利用深度学习技术将此过程展开为迭代分解网络的形式。然后构建非局部能谱相似特征引导的代价函数，约束网络的训练过程。利用双能CT真实病人数据所建立的基物质分解数据集进行评估。将NSSD-Net与2种传统模型驱动的基物质分解方法、1种基于数据驱动的基物质分解方法以及1种基于数据-模型耦合驱动的监督分解方法进行对比实验。结果 与传统模型驱动的基物质分解方法以及数据驱动的基物质分解方法相比，NSSD-Net方法在水和骨基物质分解结果中均获得最高的PNSR指标（31.383和31.444）、最高的SSIM指标（0.970和0.963）以及最低的RMSE指标（2.901和1.633）；与数据-模型耦合驱动的监督分解方法相比，NSSD-Net方法在水和骨基物质分解结果中均获得最高的SSIM指标；临床影像专家的主观图像质量评估结果显示，NSSD-Net方法在水和骨基物质分解结果中图像质量评分均最高（8.625和8.250），与其他4种对比方法分解性能之间的差异具有统计学意义（P<0.001）。结论 本方法可以获得高质量的基物质分解结果，有效避免训练数据质量问题和模型不可解释问题。

关键词: 能谱CT；基物质分解方法；深度学习；非局部能谱相似性

Abstract: Objective To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images. Methods We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method. Results The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the data-model coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P<0.001). Conclusion The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.

Key words: spectral CT; material decomposition methods; deep learning; nonlocal spectral