Show simple item record

dc.contributor.authorLombardo, Een
dc.contributor.authorDhont, Jen
dc.contributor.authorPage, Dennisen
dc.contributor.authorGaribaldi, Cen
dc.contributor.authorKünzel, LAen
dc.contributor.authorHurkmans, Cen
dc.contributor.authorTijssen, RHNen
dc.contributor.authorPaganelli, Cen
dc.contributor.authorLiu, PZYen
dc.contributor.authorKeall, PJen
dc.contributor.authorRiboldi, Men
dc.contributor.authorKurz, Cen
dc.contributor.authorLandry, Gen
dc.contributor.authorCusumano, Den
dc.contributor.authorFusella, Men
dc.contributor.authorPlacidi, Len
dc.date.accessioned2023-12-28T16:04:54Z
dc.date.available2023-12-28T16:04:54Z
dc.date.issued2023en
dc.identifier.citationLombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, et al. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 2023 Oct 26;190:109970. PubMed PMID: 37898437. Epub 2023/10/29. eng.en
dc.identifier.pmid37898437en
dc.identifier.doi10.1016/j.radonc.2023.109970en
dc.identifier.urihttp://hdl.handle.net/10541/626762
dc.description.abstractMRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1016/j.radonc.2023.109970en
dc.titleReal-time motion management in MRI-guided radiotherapy: current status and AI-enabled prospectsen
dc.typeArticleen
dc.contributor.departmentUniversity of Manchester, Division of Cancer Sciences, Manchester, United Kingdom.en
dc.identifier.journalRadiotherapy and Oncologyen
dc.description.noteen]


Files in this item

This item appears in the following Collection(s)

Show simple item record