Cone beam computed tomography (CBCT) serves as a vital imaging modality in diverse clinical applications, but is constrained by inherent limitations such as reduced image quality and increased noise. In contrast, computed tomography (CT) offers superior resolution and tissue contrast. Bridging the gap between these modalities through CBCT-to-CT synthesis becomes imperative. Deep learning techniques have enhanced this synthesis, yet challenges with generative adversarial networks persist. Denoising Diffusion Probabilistic Models have emerged as a promising alternative in image synthesis. In this study, we propose a novel texture-preserving diffusion model for CBCT-to-CT synthesis that incorporates adaptive high-frequency optimization and a dual-mode feature fusion module. Our method aims to enhance high-frequency details, effectively fuse cross-modality features, and preserve fine image structures. Extensive validation demonstrates superior performance over existing methods, showcasing better generalization. The proposed model offers a transformative pathway to augment diagnostic accuracy and refine treatment planning across various clinical settings. This work represents a pivotal step toward non-invasive, safer, and high-quality CBCT-to-CT synthesis, advancing personalized diagnostic imaging practices.

中文翻译：

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用于 CBCT-to-CT 合成的纹理保留扩散模型


锥形束计算机断层扫描 （CBCT） 在各种临床应用中是一种重要的成像方式，但受到图像质量降低和噪声增加等固有限制的限制。相比之下，计算机断层扫描 （CT） 提供卓越的分辨率和组织对比度。通过 CBCT 到 CT 合成弥合这些方式之间的差距变得势在必行。深度学习技术增强了这种综合，但生成对抗网络的挑战仍然存在。去噪扩散概率模型已成为图像合成中一种很有前途的替代方案。在这项研究中，我们提出了一种用于 CBCT-to-CT 合成的新型纹理保留扩散模型，该模型结合了自适应高频优化和双模式特征融合模块。我们的方法旨在增强高频细节，有效融合跨模态特征，并保留精细的图像结构。广泛的验证表明，与现有方法相比，性能更胜一筹，显示出更好的泛化能力。拟议的模型提供了一种变革性的途径，可以提高诊断准确性并改进各种临床环境中的治疗计划。这项工作代表了朝着无创、更安全和高质量的 CBCT 到 CT 合成迈出的关键一步，推进了个性化诊断成像实践。