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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2403.10183 |
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| _version_ | 1866914846198464512 |
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| author | Vazia, Corentin Bousse, Alexandre Froment, Jacques Vedel, Béatrice Vermet, Franck Wang, Zhihan Dassow, Thore Tasu, Jean-Pierre Visvikis, Dimitris |
| author_facet | Vazia, Corentin Bousse, Alexandre Froment, Jacques Vedel, Béatrice Vermet, Franck Wang, Zhihan Dassow, Thore Tasu, Jean-Pierre Visvikis, Dimitris |
| contents | This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse problems. Spectral CT and more particularly photon-counting CT (PCCT) can perform transmission measurements at different energy levels which can be used for material decomposition. It is an ill-posed inverse problem and therefore requires regularization. DMs are a class of generative model that can be used to solve inverse problems via diffusion posterior sampling (DPS). In this paper we adapt DPS for material decomposition in a PCCT setting. We propose two approaches, namely Two-step Diffusion Posterior Sampling (TDPS) and One-step Diffusion Posterior Sampling (ODPS). Early results from an experiment with simulated low-dose PCCT suggest that DPSs have the potential to outperform state-of-the-art model-based iterative reconstruction (MBIR). Moreover, our results indicate that TDPS produces material images with better peak signal-to-noise ratio (PSNR) than images produced with ODPS with similar structural similarity (SSIM). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10183 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spectral CT Two-step and One-step Material Decomposition using Diffusion Posterior Sampling Vazia, Corentin Bousse, Alexandre Froment, Jacques Vedel, Béatrice Vermet, Franck Wang, Zhihan Dassow, Thore Tasu, Jean-Pierre Visvikis, Dimitris Medical Physics This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse problems. Spectral CT and more particularly photon-counting CT (PCCT) can perform transmission measurements at different energy levels which can be used for material decomposition. It is an ill-posed inverse problem and therefore requires regularization. DMs are a class of generative model that can be used to solve inverse problems via diffusion posterior sampling (DPS). In this paper we adapt DPS for material decomposition in a PCCT setting. We propose two approaches, namely Two-step Diffusion Posterior Sampling (TDPS) and One-step Diffusion Posterior Sampling (ODPS). Early results from an experiment with simulated low-dose PCCT suggest that DPSs have the potential to outperform state-of-the-art model-based iterative reconstruction (MBIR). Moreover, our results indicate that TDPS produces material images with better peak signal-to-noise ratio (PSNR) than images produced with ODPS with similar structural similarity (SSIM). |
| title | Spectral CT Two-step and One-step Material Decomposition using Diffusion Posterior Sampling |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2403.10183 |