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EUReCA: End-to-End Unified CBCT Reconstruction Across Clinical Acquisitions — arXiv:2610.37740

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.37740
License (as archived): CC0-1.0
Archived: 2026-10-31T06:11:03.796Z
SHA-256 of the archived text: a8bd5e50dc0666ff758d18cb38201d08183117aadf76f5916cd47d0c48c40472
Stable URL of this page: https://metraipolis.com/documento/dc_9024faebbbd1

How to cite

EUReCA: End-to-End Unified CBCT Reconstruction Across Clinical Acquisitions — arXiv:2610.37740 — arXiv (en), https://arxiv.org/abs/2610.37740 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9024faebbbd1 (Metraipolis archive dc_9024faebbbd1, sha256 a8bd5e50dc0666ff758d18cb38201d08183117aadf76f5916cd47d0c48c40472)

BibTeX

@misc{dc9024faebbbd1,
  title = {EUReCA: End-to-End Unified CBCT Reconstruction Across Clinical Acquisitions — arXiv:2610.37740},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.37740},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9024faebbbd1; sha256 a8bd5e50dc0666ff758d18cb38201d08183117aadf76f5916cd47d0c48c40472},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.37740. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cone-beam computed tomography (CBCT) is widely used for image-guided radiotherapy, but reducing the number of projections to lower dose and scan time makes reconstruction increasingly ill-posed. We present EUReCA (End-to-end Unified REconstruction across Clinical Acquisitions), a geometry-aware feed-forward model for variable-view clinical linac CBCT. For a given acquisition trajectory, EUReCA accepts 8-100 projections with a single set of weights, without view-count-specific retraining; the same architecture and training recipe extend unchanged to other clinical trajectories. It combines learned projection features with an in-frame Feldkamp-Davis-Kress (FDK) reconstruction. The model encodes each projection with a shared multiscale backbone, back-projects features into multiresolution 3D grids using the acquisition geometry, aggregates variable numbers of views at fixed computational cost, and refines the volume with a global 3D transformer and multilevel decoder. On simulated data, EUReCA achieved 29.9-33.2 dB PSNR with 10-100 views and outperformed all applicable feed-forward baselines; it also exceeded iterative optimization-based methods while running orders of magnitude faster. On 15 measured patient CBCT scans evaluated against vendor reconstructions, EUReCA achieved the best performance at every tested view count. These results suggest that geometry-aware, measurement-anchored feed-forward reconstruction can provide a practical and flexible foundation for variable-view linac CBCT.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Image and Video Processing (eess.IV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.37740 — 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 2026-10-31T06:07:08.594Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).