用扩散模型将低质头颈CBCT转为高精度CT，提升放疗图像质量。
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
- 基于旋转等变的扩散生成模型，保留解剖细节并抑制伪影。
- 在SynthRAD2025数据集上，结构保真度与HU值准确率显著优于对比方法。
- 适合需要实时高精度影像的自适应放疗场景使用。
锥形束计算机断层扫描（CBCT）常用于图像引导放疗，具有实时性、低成本和低辐射剂量的优点，但受光子散射和束遮挡影响，图像存在伪影，导致亨氏单位（HU）校准不准，影响剂量计算与自适应计划可靠性。相比之下，普通计算机断层扫描（CT）图像质量更高、HU校准准确，但通常为离线采集，无法捕捉治疗过程中的解剖变化。因此，亟需发展从CBCT到CT的高精度图像合成方法以弥合这一差距。本文提出一种新型基于扩散的概率生成模型EqDiff-CT，采用去噪扩散概率模型（DDPM）迭代加噪并学习潜在表示，实现解剖一致的高质量CT重建。其核心采用带有e2cnn可转向层的群等变条件U-Net主干网络，强制施加循环对称性（C4），有效保留细微结构特征，同时减少噪声与伪影。模型在包含多个头颈部解剖部位的SynthRAD2025数据集上训练与验证，与CycleGAN、DDPM等先进方法对比，表现出显著提升的结构保真度、HU值准确性及定量指标。视觉评估显示，新方法能更好恢复组织边界，实现更真实的骨结构重建。结果表明，该扩散模型为提升CBCT质量提供了鲁棒且通用的框架。
原文摘要 · Abstract (English)
Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical visualization with low acquisition cost and dose. Nevertheless, photon scattering and beam hindrance lead CBCT images to suffer from several artifacts. These involve inaccurate Hounsfield Unit HU values, which render a lower reliability towards the dose calculations and adaptive planning. Purpose: Computed tomography CT, on the contrary, offers better image quality and accurate HU calibration, yet is typically acquired using offline mode and fails to capture the intra-treatment anatomical changes. This renders a need for developing an accurate CBCT to CT synthesis to mitigate the gap in imaging quality in the adaptive radiotherapy workflow. Methods: We propose a novel diffusion based conditional generative model, coined EqDiff-CT, to synthesize high quality CT images from CBCT. EqDiff-CT employs a denoising diffusion probabilistic model DDPM to iteratively inject noise and learn latent representations that enable reconstruction of anatomically consistent CT images. A group equivariant conditional U-Net backbone, implemented with e2cnn steerable layers, enforces rotational equivariance cyclic C4 symmetry, helping preserve fine structural details while minimizing noise and artifacts. Results: The system was trained and validated on the SynthRAD2025 dataset, comprising CBCT-CT scans across multiple head and neck anatomical sites, and we compared it with advanced methods such as CycleGAN and DDPM. EqDiff-CT provided substantial gains in structural fidelity, HU accuracy and quantitative metrics. Visual findings confirm the improved recovery, sharper soft tissue boundaries, and realistic bone reconstructions. Conclusions: The findings suggest that the diffusion model has offered a robust and generalizable framework for CBCT improvements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。