Xiao Ning, Yang Wanting, Qiang Yan, Zhao Juanjuan, Hao Rui, Lian Jianhong, Li Shuo
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
School of Information Management, Shanxi University of Finance and Economics, Taiyuan, China.
Front Med (Lausanne). 2022 Mar 31;9:792390. doi: 10.3389/fmed.2022.792390. eCollection 2022.
The fusion of PET metabolic images and CT anatomical images can simultaneously display the metabolic activity and anatomical position, which plays an indispensable role in the staging diagnosis and accurate positioning of lung cancer.
In order to improve the information of PET-CT fusion image, this article proposes a PET-CT fusion method Siamese Pyramid Fusion Network (SPFN). In this method, feature pyramid transformation is introduced to the siamese convolution neural network to extract multi-scale information of the image. In the design of the objective function, this article considers the nature of image fusion problem, utilizes the image structure similarity as the objective function and introduces L1 regularization to improve the quality of the image.
The effectiveness of the proposed method is verified by more than 700 pairs of PET-CT images and elaborate experimental design. The visual fidelity after fusion reaches 0.350, the information entropy reaches 0.076.
The quantitative and qualitative results proved that the proposed PET-CT fusion method has some advantages. In addition, the results show that PET-CT fusion image can improve the ability of staging diagnosis compared with single modal image.
PET代谢图像与CT解剖图像的融合能够同时显示代谢活性和解剖位置，在肺癌的分期诊断及精确定位中发挥着不可或缺的作用。
为了提升PET-CT融合图像的信息，本文提出了一种PET-CT融合方法——暹罗金字塔融合网络（SPFN）。在此方法中，将特征金字塔变换引入到暹罗卷积神经网络中以提取图像的多尺度信息。在目标函数的设计上，本文考虑了图像融合问题的本质，利用图像结构相似性作为目标函数并引入L1正则化来提升图像质量。
通过700多对PET-CT图像及精心设计的实验验证了所提方法的有效性。融合后的视觉保真度达到0.350，信息熵达到0.076。
定量和定性结果证明所提的PET-CT融合方法具有一定优势。此外，结果表明PET-CT融合图像相比于单模态图像能够提高分期诊断能力。
