用可插拔梯度步模型降低牙科锥形束CT的光子噪声
Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction
- 基于模拟投影数据训练梯度步去噪器作为先验
- 在合成数据上显著降低噪声，在真实图像上表现良好
- 适合需要低剂量成像的口腔CT重建场景
本研究旨在降低牙科锥形束CT重建中的光子噪声。通过建立逆问题求解框架，构建基于数据的先验模型。具体方法是模拟扇形束采集并添加光子噪声到投影数据，利用重建后的模拟数据训练一个梯度步去噪器作为先验。训练好的模型被集成进可插拔梯度步算法中，用于从模拟投影数据中重建图像。在合成数据上的实验验证了该模型的去噪能力，而对真实图像的定性评估则展示了算法的性能与泛化能力。
原文摘要 · Abstract (English)
The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisitions and add photon noise to the projection data. The prior is obtained by training a gradient-step denoiser using reconstructed simulated acquisitions. The trained model is integrated into a plug-and-play gradient-step algorithm to reconstruct images from simulated projections. Experiments on synthetic data demonstrate the denoising capabilities of the trained model, while qualitative evaluations on real images showcase the algorithm's performance and generalization ability.
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