Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Fernanda Villegas - , Karolinska Institutet (Author)
  • Riccardo Dal Bello - , University of Zurich (Author)
  • Emilie Alvarez-Andres - , Department of Radiotherapy and Radiooncology, OncoRay - National Center for Radiation Research in Oncology (Author)
  • Jennifer Dhont - , Université libre de Bruxelles (ULB) (Author)
  • Tomas Janssen - , Netherlands Cancer Institute (Author)
  • Lisa Milan - , Ente Ospedaliero Cantonale (EOC) (Author)
  • Charlotte Robert - , Institut Gustave Roussy (Author)
  • Ghizela Ana Maria Salagean - , Babes-Bolyai University, TopMed Medical Centre (Author)
  • Natalia Tejedor - , Hospital de la Santa creu i Sant Pau (Author)
  • Petra Trnková - , Medical University of Vienna (Author)
  • Marco Fusella - , Abano Terme Hospital (Author)
  • Lorenzo Placidi - , A. Gemelli University Hospital Foundation IRCCS (Author)
  • Davide Cusumano - , Mater Olbia Hospital (MOH) (Author)

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.

Details

Original languageEnglish
Article number110387
JournalRadiotherapy and oncology
Volume198
Publication statusPublished - Sept 2024
Peer-reviewedYes

External IDs

PubMed 38885905

Keywords

Keywords

  • Artificial intelligence, Clinical implementation, Deep learning, MR-only planning, MR-only radiotherapy, Synthetic CT