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/intelland/GLFC

本研究解决了现有合成CT生成方法在有效捕捉全球和局部特征与对比方面的不足。提出的GLFC框架利用Mamba增强UNet进行特征学习，并通过多对比损失函数提高合成CT的质量，实验结果显示其在SSIM指标上实现了显著提升。该研究有望提升CBCT图像的质量，为临床应用提供更高精度的合成CT图像。

GLFC：结合全球-局部特征与对比学习的统一框架，通过Mamba增强UNet从CBCT生成合成CT