Li Jitao, Qu Zongjin, Yang Yue, Zhang Fuchun, Li Meng, Hu Shunbo
College of Information Science and Engineering, Linyi University, Linyi, 276000, China.
College of Chemistry and Chemical Engineering, Linyi University, Linyi, 276000, China.
Biomed Opt Express. 2022 Oct 24;13(11):6003-6018. doi: 10.1364/BOE.467683. eCollection 2022 Nov 1.
Multimodal medical images can be used in a multifaceted approach to resolve a wide range of medical diagnostic problems. However, these images are generally difficult to obtain due to various limitations, such as cost of capture and patient safety. Medical image synthesis is used in various tasks to obtain better results. Recently, various studies have attempted to use generative adversarial networks for missing modality image synthesis, making good progress. In this study, we propose a generator based on a combination of transformer network and a convolutional neural network (CNN). The proposed method can combine the advantages of transformers and CNNs to promote a better detail effect. The network is designed for positron emission tomography (PET) to computer tomography synthesis, which can be used for PET attenuation correction. We also experimented on two datasets for magnetic resonance T1- to T2-weighted image synthesis. Based on qualitative and quantitative analyses, our proposed method outperforms the existing methods.
多模态医学图像可用于多方面的方法来解决广泛的医学诊断问题。然而，由于各种限制，如采集成本和患者安全，这些图像通常难以获得。医学图像合成用于各种任务以获得更好的结果。最近，各种研究尝试使用生成对抗网络进行缺失模态图像合成，并取得了良好进展。在本研究中，我们提出了一种基于变压器网络和卷积神经网络（CNN）组合的生成器。所提出的方法可以结合变压器和CNN的优点，以促进更好的细节效果。该网络专为正电子发射断层扫描（PET）到计算机断层扫描合成而设计，可用于PET衰减校正。我们还在两个数据集上进行了磁共振T1加权到T2加权图像合成的实验。基于定性和定量分析，我们提出的方法优于现有方法。
