标题： GLFC：基于Mamba增强的UNet的统一全局-局部特征与对比学习用于从CBCT生成合成CT 显示英文标题

标题： GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT

摘要： 从锥形束计算机断层扫描（CBCT）生成合成计算机断层扫描（CT）图像对于提高CBCT的图像质量是有益的。使用卷积神经网络（CNN）和变换器的现有合成CT（sCT）生成方法在有效捕捉全局和局部特征及对比度以生成高质量sCT方面常常面临困难。在本工作中，我们提出了一种全局-局部特征和对比学习（GLFC）框架用于sCT生成。首先，通过将Mamba块集成到高分辨率UNet的跳跃连接中，引入了一种Mamba增强UNet（MEUNet），以有效进行全局和局部特征学习。其次，我们提出了一种多对比损失（MCL），在不同的强度窗口计算合成损失，以提高软组织和骨区域的质量。在SynthRAD2023数据集上的实验表明，与原始CBCT相比，GLFC将sCT的SSIM从77.91%提高到了91.50%，并且在sCT生成方面显著优于几种现有方法。代码可在https://github.com/HiLab-git/GLFC获取。 显示英文摘要

摘要： Generating synthetic Computed Tomography (CT) images from Cone Beam Computed Tomography (CBCT) is desirable for improving the image quality of CBCT. Existing synthetic CT (sCT) generation methods using Convolutional Neural Networks (CNN) and Transformers often face difficulties in effectively capturing both global and local features and contrasts for high-quality sCT generation. In this work, we propose a Global-Local Feature and Contrast learning (GLFC) framework for sCT generation. First, a Mamba-Enhanced UNet (MEUNet) is introduced by integrating Mamba blocks into the skip connections of a high-resolution UNet for effective global and local feature learning. Second, we propose a Multiple Contrast Loss (MCL) that calculates synthetic loss at different intensity windows to improve quality for both soft tissues and bone regions. Experiments on the SynthRAD2023 dataset demonstrate that GLFC improved the SSIM of sCT from 77.91% to 91.50% compared with the original CBCT, and significantly outperformed several existing methods for sCT generation. The code is available at https://github.com/HiLab-git/GLFC