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

TL;DR

This paper introduces PWD, a novel diffusion model that uses prior guidance and wavelet feature fusion to efficiently reconstruct high-quality limited-angle CT images with fewer sampling steps, preserving details.

Contribution

The proposed PWD model combines prior-guided sampling and wavelet-based multi-scale feature fusion to improve efficiency and detail preservation in limited-angle CT reconstruction.

Findings

Outperforms existing methods in PSNR and SSIM metrics.

Achieves high-quality reconstructions with only 50 sampling steps.

Effectively preserves fine structural details in clinical datasets.

Abstract

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.