Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866917538585116672 |
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| author | Tatachak, Idris Kabongo, Luis Papadakis, Nicolas Ripoche, Xavier Rit, Simon |
| author_facet | Tatachak, Idris Kabongo, Luis Papadakis, Nicolas Ripoche, Xavier Rit, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28124 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction Tatachak, Idris Kabongo, Luis Papadakis, Nicolas Ripoche, Xavier Rit, Simon Artificial Intelligence 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. |
| title | Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.28124 |