eprintid: 10203159 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/20/31/59 datestamp: 2025-01-10 10:35:03 lastmod: 2025-01-10 10:35:03 status_changed: 2025-01-10 10:35:03 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Huang, Yuliang creators_name: Eiben, Bjoern creators_name: Thielemans, Kris creators_name: McClelland, Jamie R title: Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography ispublished: pub divisions: UCL divisions: B02 divisions: B04 divisions: C10 divisions: D17 divisions: F42 divisions: FI6 keywords: 4DCT, Irregular breath, Motion model note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: 4D Computed Tomography (4DCT) is widely used for many clinical applications such as radiotherapy treatment planning, PET and ventilation imaging. However, common 4DCT methods reconstruct multiple breath cycles into a single, arbitrary breath cycle which can lead to various artefacts, impacting the downstream clinical applications. Surrogate driven motion models can estimate continuous variable motion across multiple cycles based on CT segments ‘unsorted’ from 4DCT, but it requires respiration surrogate signals with strong correlation to the internal motion, which are not always available. The method proposed in this study eliminates such dependency by adapting the hyper-gradient method to the optimization of surrogate signals as hyper-parameters, while achieving better or comparable performance, as demonstrated on digital phantom simulations and real patient data. Our method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition. Our method has the potential to improve downstream clinical applications, and also enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored. Code is available at https://github.com/Yuliang-Huang/4DCT-irregular-motion. date: 2024-10-03 date_type: published publisher: Springer, Cham official_url: https://doi.org/10.1007/978-3-031-72378-0_55 full_text_type: other language: eng verified: verified_manual elements_id: 2349780 doi: 10.1007/978-3-031-72378-0_55 isbn_13: 978-3-031-72377-3 lyricists_name: Thielemans, Kris lyricists_name: McClelland, James lyricists_name: Huang, Yuliang lyricists_id: KTHIE60 lyricists_id: JRMCC68 lyricists_id: YYHUA54 actors_name: Huang, Yuliang actors_id: YYHUA54 actors_role: owner full_text_status: restricted pres_type: paper series: Lecture Notes in Computer Science, vol 15001 publication: MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT I volume: 15001 pagerange: 588-597 pages: 10 event_title: 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) event_location: MOROCCO, Palmeraie Conf Ctr, Marrakesh event_dates: 6 Oct 2024 - 10 Oct 2024 issn: 0302-9743 book_title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 editors_name: Linguraru, MG editors_name: Dou, Q editors_name: Feragen, A editors_name: Giannarou, S editors_name: Glocker, B editors_name: Lekadir, K editors_name: Schnabel, JA citation: Huang, Yuliang; Eiben, Bjoern; Thielemans, Kris; McClelland, Jamie R; (2024) Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography. In: Linguraru, MG and Dou, Q and Feragen, A and Giannarou, S and Glocker, B and Lekadir, K and Schnabel, JA, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. (pp. pp. 588-597). Springer, Cham document_url: https://discovery.ucl.ac.uk/id/eprint/10203159/1/Paper-3437.pdf