Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. (May 2021)

• Record Type: Journal Article
• Authors: Cusumano, Davide; Boldrini, Luca; Dhont, Jennifer; Fiorino, Claudio; Green, Olga; Güngör, Görkem; Jornet, Núria; Klüter, Sebastian; Landry, Guillaume; Mattiucci, Gian Carlo; Placidi, Lorenzo; Reynaert, Nick; Ruggieri, Ruggero; Tanadini-Lang, Stephanie; Thorwarth, Daniela; Yadav, Poonam; Yang, Yingli; Valentini, Vincenzo; Verellen, Dirk; Indovina, Luca
• Is Part Of: Physica medica. Volume 85(2021)
• Page Start: 175
• Page End: 191
• Publication Date: 2021-05
• Subjects: Artificial Intelligence -- Deep learning -- MR-guided Radiotherapy -- Online Adaptive Radiotherapy -- MR-Linac; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.05.010
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Technological innovation in Radiotherapy led to Magnetic Resonance guided RT systems. Artificial Intelligence can play a significant role in MR guided RT. Many manual procedures can be automatized and standardized thanks to AI. Medical Physicists are expected to be major actors in AI clinical implementation. The current status of AI integration in MRgRT will be presented and discussed. Abstract: Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.