Diffusion-based Sinogram Interpolation for Limited Angle PET
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918198874472448 |
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| author | Yilmaz, Rüveyda Thull, Julian Stegmaier, Johannes Schulz, Volkmar |
| author_facet | Yilmaz, Rüveyda Thull, Julian Stegmaier, Johannes Schulz, Volkmar |
| contents | Accurate PET imaging increasingly requires methods that support unconstrained detector layouts from walk-through designs to long-axial rings where gaps and open sides lead to severely undersampled sinograms. Instead of constraining the hardware to form complete cylinders, we propose treating the missing lines-of-responses as a learnable prior. Data-driven approaches, particularly generative models, offer a promising pathway to recover this missing information. In this work, we explore the use of conditional diffusion models to interpolate sparsely sampled sinograms, paving the way for novel, cost-efficient, and patient-friendly PET geometries in real clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion-based Sinogram Interpolation for Limited Angle PET Yilmaz, Rüveyda Thull, Julian Stegmaier, Johannes Schulz, Volkmar Machine Learning Accurate PET imaging increasingly requires methods that support unconstrained detector layouts from walk-through designs to long-axial rings where gaps and open sides lead to severely undersampled sinograms. Instead of constraining the hardware to form complete cylinders, we propose treating the missing lines-of-responses as a learnable prior. Data-driven approaches, particularly generative models, offer a promising pathway to recover this missing information. In this work, we explore the use of conditional diffusion models to interpolate sparsely sampled sinograms, paving the way for novel, cost-efficient, and patient-friendly PET geometries in real clinical settings. |
| title | Diffusion-based Sinogram Interpolation for Limited Angle PET |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.09383 |