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Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

Authors :
Lombardo, Elia
Dhont, Jennifer
Page, Denis
Garibaldi, Cristina
Künzel, Luise A.
Hurkmans, Coen
Tijssen, Rob H.N.
Paganelli, Chiara
Liu, Paul Z.Y.
Keall, Paul J.
Riboldi, Marco
Kurz, Christopher
Landry, Guillaume
Cusumano, Davide
Fusella, Marco
Placidi, Lorenzo
Source :
Radiotherapy & Oncology. Jan2024, Vol. 190, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• Description of intra-fractional motion management workflow in MRI-guided radiotherapy. • Envisioned workflow with both highest dosimetric accuracy and duty cycle efficiency. • Focus on real-time implementation based on artificial intelligence. MRI-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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01678140
Volume :
190
Database :
Academic Search Index
Journal :
Radiotherapy & Oncology
Publication Type :
Academic Journal
Accession number :
175498657
Full Text :
https://doi.org/10.1016/j.radonc.2023.109970