{"ID":2842864,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09383","arxiv_id":"2511.09383","title":"Diffusion-based Sinogram Interpolation for Limited Angle PET","abstract":"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.","short_abstract":"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-respon...","url_abs":"https://arxiv.org/abs/2511.09383","url_pdf":"https://arxiv.org/pdf/2511.09383v1","authors":"[\"Rüveyda Yilmaz\",\"Julian Thull\",\"Johannes Stegmaier\",\"Volkmar Schulz\"]","published":"2025-11-12T14:50:42Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}