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SinoDiff: Physics-Consistent Self-Supervised Diffusion for Unified Low-Dose to Standard-Dose PET Sinogram Recovery — arXiv:2608.11514

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11514
License (as archived): CC0-1.0
Archived: 2026-10-19T06:07:26.221Z
SHA-256 of the archived text: 99585f150c0ee0fca06b446505ef6a387104192ceee50d39399b90bf20889fbc
Stable URL of this page: https://metraipolis.com/documento/dc_08df3cb62007

How to cite

SinoDiff: Physics-Consistent Self-Supervised Diffusion for Unified Low-Dose to Standard-Dose PET Sinogram Recovery — arXiv:2608.11514 — arXiv (en), https://arxiv.org/abs/2608.11514 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_08df3cb62007 (Metraipolis archive dc_08df3cb62007, sha256 99585f150c0ee0fca06b446505ef6a387104192ceee50d39399b90bf20889fbc)

BibTeX

@misc{dc08df3cb62007,
  title = {SinoDiff: Physics-Consistent Self-Supervised Diffusion for Unified Low-Dose to Standard-Dose PET Sinogram Recovery — arXiv:2608.11514},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11514},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_08df3cb62007; sha256 99585f150c0ee0fca06b446505ef6a387104192ceee50d39399b90bf20889fbc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11514. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Low-dose positron emission tomography (LD-PET) reduces radiation exposure but leads to poor image quality and hinders diagnostic confidence. Existing supervised LD to standard-dose (SD) PET recovery methods often fail to generalise across dose variations, while dose-agnostic supervised methods require paired LD-SD data. Current self-supervised methods, although more flexible, typically produce inferior results, including loss of anatomical details and oversmoothing pathological features. These pose major limitations for practical applications. To overcome these limitations, we propose SinoDiff, a novel self-supervised, physics-consistent diffusion framework for the recovery of PET sinograms across multiple predefined dose levels. Unlike the noise simulation in traditional diffusion methods, SinoDiff integrates the PET acquisition model into the forward diffusion process via Poisson thinning, enabling physically consistent sampling/modelling of dose-dependent count statistics. During the reverse diffusion process, SinoDiff estimates the incremental change in PET signals from predefined dose levels. Therefore, SinoDiff is a single, unified model that requires no retraining across multiple dose levels. To consider the characteristics of PET sinogram, we incorporate a frequency-domain convolution to capture long-range dependencies across projection angles and detector bins. Experiments on [18F]-FDG and [18F]-FDOPA datasets demonstrate that SinoDiff achieves competitive performance against supervised and self-supervised baselines across multiple dose levels.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-13 in eess.IV).
Categorie arXiv: Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2608.11514 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:56:53.281Z dalla pagina https://arxiv.org/catchup/eess.IV/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).