发布日期: 2025-07-11

更新日期: 2025-07-12

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2025-07-11 更新

PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CT

Authors:Yi Liu, Yiyang Wen, Zekun Zhou, Junqi Ma, Linghang Wang, Yucheng Yao, Liu Shi, Qiegen Liu

Generative diffusion models have received increasing attention in medical imaging, particularly in limited-angle computed tomography (LACT). Standard diffusion models achieve high-quality image reconstruction but require a large number of sampling steps during inference, resulting in substantial computational overhead. Although skip-sampling strategies have been proposed to improve efficiency, they often lead to loss of fine structural details. To address this issue, we propose a prior information embedding and wavelet feature fusion fast sampling diffusion model for LACT reconstruction. The PWD enables efficient sampling while preserving reconstruction fidelity in LACT, and effectively mitigates the degradation typically introduced by skip-sampling. Specifically, during the training phase, PWD maps the distribution of LACT images to that of fully sampled target images, enabling the model to learn structural correspondences between them. During inference, the LACT image serves as an explicit prior to guide the sampling trajectory, allowing for high-quality reconstruction with significantly fewer steps. In addition, PWD performs multi-scale feature fusion in the wavelet domain, effectively enhancing the reconstruction of fine details by leveraging both low-frequency and high-frequency information. Quantitative and qualitative evaluations on clinical dental arch CBCT and periapical datasets demonstrate that PWD outperforms existing methods under the same sampling condition. Using only 50 sampling steps, PWD achieves at least 1.7 dB improvement in PSNR and 10% gain in SSIM.

生成扩散模型在医学成像中受到了越来越多的关注，特别是在有限角度计算机断层扫描（LACT）中。标准扩散模型能够实现高质量图像重建，但在推理过程中需要大量采样步骤，导致计算开销很大。虽然提出了跳过采样策略来提高效率，但它们往往会导致精细结构细节的丢失。为了解决这一问题，我们提出了一种基于先验信息嵌入和小波特征融合的快速采样扩散模型，用于LACT重建。PWD（Prior Wavelet Diffusion）能够在LACT中实现高效采样，同时保持重建保真度，并有效减轻跳过采样通常引起的降解。具体而言，在训练阶段，PWD将LACT图像的分布映射到完全采样的目标图像分布，使模型能够学习两者之间的结构对应关系。在推理阶段，LACT图像作为明确的先验来引导采样轨迹，允许用更少的步骤实现高质量重建。此外，PWD在小波域进行多尺度特征融合，通过利用低频和高频信息，有效地提高了精细细节的重建效果。在临床牙科全景CBCT和根尖周数据集上的定量和定性评估表明，在相同的采样条件下，PWD优于现有方法。仅使用50个采样步骤，PWD的PSNR提高了至少1.7 dB，SSIM提高了10%。

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Summary
针对有限角度计算机断层扫描（LACT）重建问题，提出一种基于先验信息嵌入和小波特征融合的快速采样扩散模型。该模型在训练阶段学习LACT图像与全采样目标图像的结构对应关系，在推理阶段利用LACT图像作为显式先验来指导采样轨迹，实现高效采样并保持重建保真度，有效减轻跳过采样引起的降解。此外，该模型还进行多尺度特征融合，利用高低频信息增强精细细节的重建。临床牙弓CBCT和根尖周数据集上的定量和定性评估表明，在相同采样条件下，PWD较现有方法表现更优，仅在50个采样步骤下，PWD的PSNR提高至少1.7 dB，SSIM提高10%。

Key Takeaways