用扩散模型从CBCT生成高质量CT图像，提升放疗精度。
Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review
- 基于条件扩散模型，通过迭代优化生成更清晰的CT图像
- 在降噪和伪影抑制上优于传统深度学习方法，尤其在肺部成像中表现突出
- 适合放疗计划、剂量计算等临床场景，但实时性仍需改进
锥形束CT（CBCT）是低剂量成像的替代方案，但存在噪声、散射和伪影，影响图像质量。合成CT（sCT）旨在将CBCT转换为高保真度的CT图像，以提高解剖准确性和剂量计算精度。尽管深度学习方法已展现出潜力，但在泛化能力和细节保留方面仍受限。条件扩散模型（CDMs）凭借其迭代优化过程，提供新解决方案。本综述系统分析了2013至2024年间在Web of Science、Scopus和Google Scholar中检索到的11篇使用CDMs进行CBCT到sCT生成的研究。结果显示，结合解剖先验和空间频域特征的CDMs在结构保持和抗噪能力上表现更优；能量引导与混合潜空间模型提升了剂量准确性与个性化合成效果。总体上，CDMs在噪声抑制和伪影消除方面持续优于传统深度学习模型，尤其在肺部成像及双能CT等挑战性场景中。结论指出，条件扩散模型在通用、精准的sCT生成方面具有强潜力，但临床应用仍有限。未来研究应聚焦可扩展性、实时推理与多模态影像融合，以增强临床相关性。
原文摘要 · Abstract (English)
Objective: Cone-beam computed tomography (CBCT) provides a low-dose imaging alternative to conventional CT, but suffers from noise, scatter, and artifacts that degrade image quality. Synthetic CT (sCT) aims to translate CBCT to high-quality CT-like images for improved anatomical accuracy and dosimetric precision. Although deep learning approaches have shown promise, they often face limitations in generalizability and detail preservation. Conditional diffusion models (CDMs), with their iterative refinement process, offers a novel solution. This review systematically examines the use of CDMs for CBCT-to-sCT synthesis. Methods: A systematic search was conducted in Web of Science, Scopus, and Google Scholar for studies published between 2013 and 2024. Inclusion criteria targeted works employing conditional diffusion models specifically for sCT generation. Eleven relevant studies were identified and analyzed to address three questions: (1) What conditional diffusion methods are used? (2) How do they compare to conventional deep learning in accuracy? (3) What are their clinical implications? Results: CDMs incorporating anatomical priors and spatial-frequency features demonstrated improved structural preservation and noise robustness. Energy-guided and hybrid latent models enabled enhanced dosimetric accuracy and personalized image synthesis. Across studies, CDMs consistently outperformed traditional deep learning models in noise suppression and artefact reduction, especially in challenging cases like lung imaging and dual-energy CT. Conclusion: Conditional diffusion models show strong potential for generalized, accurate sCT generation from CBCT. However, clinical adoption remains limited. Future work should focus on scalability, real-time inference, and integration with multi-modal imaging to enhance clinical relevance.
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