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Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT — arXiv:2610.09579

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09579
License (as archived): CC0-1.0
Archived: 2026-10-08T12:47:27.854Z
SHA-256 of the archived text: 9619d5ebd2b830702a368ff2bd159205911bad784e17d0ab10cb57c974e0c726
Stable URL of this page: https://metraipolis.com/documento/dc_c9eebf64cf24

How to cite

Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT — arXiv:2610.09579 — arXiv (en), https://arxiv.org/abs/2610.09579 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c9eebf64cf24 (Metraipolis archive dc_c9eebf64cf24, sha256 9619d5ebd2b830702a368ff2bd159205911bad784e17d0ab10cb57c974e0c726)

BibTeX

@misc{dcc9eebf64cf24,
  title = {Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT — arXiv:2610.09579},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09579},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c9eebf64cf24; sha256 9619d5ebd2b830702a368ff2bd159205911bad784e17d0ab10cb57c974e0c726},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09579. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Trajectory optimisation for cone-beam computed tomography (CT) determines which information sparse-view scans acquire. Fixed candidate pools prevent off-grid refinement and require new object-specific precomputation for each acquisition manifold. We make every source pose an individual continuous variable and move all poses jointly by gradient ascent on the scanner's kinematic manifold. The objective combines soft-Tuy plane coverage, continuous View Covariance Loss, and an analytic attenuation-aware ray-bundle penalty. The same optimiser handles circular, limited C-arm, two-axis, and freesphere parametrisations. On a Defrise flange, continuous selection recovers laminar defects invisible to a circular orbit, matches discrete swap search on the free sphere at the sparser budget, and leads at the denser one, with the same objective evaluated in every arm. A moderate elevation band already recovers most of the free-sphere gain at the defects, so the same optimiser transfers to bounded scanner envelopes. Photon noise preserves the ordering on the flange and compresses it on a dense fuel nozzle. Sparseprescan planning benefits from matching prescan and planned acquisition manifolds. Selection takes seconds rather than minutes without an object-specific reconstruction basis. Prescan-planned poses were executed on a robot CT bench and reconstructed in a common frame, demonstrating feasibility but no consistent metric gain over uniform band sampling. Continuous pose optimisation incorporates attenuation and scanner constraints directly into sparse-view acquisition design.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.09579 — 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 2026-10-08T12:18:15.388Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).