Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy
Year: 2024
Type: article
Abstract: Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.
Source: Radiotherapy and Oncology
Institutions: Karolinska University Hospital, Karolinska Institutet, University of Zurich, University Hospital Zurich, Helmholtz-Zentrum Dresden-Rossendorf, OncoRay, University Hospital Carl Gustav Carus, Technische Universität Dresden, Université Libre de Bruxelles, Institut Jules Bordet, The Netherlands Cancer Institute, Ente Ospedaliero Cantonale, Istituto Imaging della Svizzera Italiana, Inserm, Université Paris-Saclay, Institut Gustave Roussy, Babeș-Bolyai University, Hospital de Sant Pau, Medical University of Vienna, Policlinico Abano Terme, Agostino Gemelli University Polyclinic
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,430
OpenAlex ID: W4399701128