Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction
For dental CT imaging, this provides a denoising method that generalizes to real data, though it is incremental as it applies existing plug-and-play techniques to a specific domain.
This work reduces photon noise in dental cone-beam CT reconstruction by training a gradient-step denoiser on simulated fan-beam acquisitions and integrating it into a plug-and-play algorithm, demonstrating denoising on synthetic data and generalization on real images.
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.