发布日期: 2025-07-12

更新日期: 2025-07-12

文章字数: 1.3k

阅读时长: 5 分

阅读次数:

⚠️ 以下所有内容总结都来自于大语言模型的能力，如有错误，仅供参考，谨慎使用
🔴 请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！
💗 如果您觉得我们的项目对您有帮助 ChatPaperFree ，还请您给我们一些鼓励！⭐️ HuggingFace免费体验

2025-07-12 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.

在医学成像中，尤其是有限角度计算机断层扫描（LACT）中，生成式扩散模型已越来越受到关注。标准扩散模型虽然能够实现高质量图像重建，但在推理过程中需要大量的采样步骤，导致计算开销很大。虽然有人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带来的退化。具体而言，在训练阶段，PWD将LACT图像的分布映射到完全采样目标图像的分布，使模型能够学习两者之间的结构对应关系。在推理阶段，LACT图像作为一个明确的先验来引导采样轨迹，允许以较少的步骤实现高质量的重建。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值提高了至少1.7dB，SSIM值提高了10%。

论文及项目相关链接

PDF

摘要

本文主要研究了在有限角度计算机断层扫描（LACT）中，生成扩散模型的应用及优化。针对标准扩散模型在推理过程中需要大量采样步骤导致计算量大、效率低的问题，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。该模型能够在保证LACT重建保真度的同时，提高采样效率，有效减轻跳采样带来的图像质量下降问题。通过训练阶段将LACT图像分布映射到完全采样目标图像分布，使模型学习到两者之间的结构对应关系。在推理阶段，利用LACT图像作为显式先验，引导采样轨迹，实现高质量重建并大大减少采样步骤。此外，该模型还实现了小波域多尺度特征融合，有效利用低频和高频信息，提升了图像细节的重建效果。临床牙科全景CBCT和根尖周数据集的实验结果表明，在同一采样条件下，该模型优于现有方法。仅使用50个采样步骤，该模型峰值信噪比（PSNR）提高了至少1.7 dB，结构相似性指数（SSIM）提高了约10%。

关键见解