Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy

Gurney-Champion, Oliver J.; Landry, Guillaume; Redalen, Kathrine Roe und Thorwarth, Daniela (2022): Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy. In: Seminars in Radiation Oncology, Bd. 32, Nr. 4: S. 377-388

Abstract

Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed for different qMRI tasks. Therefore, this article discusses the current state-of-the-art and potential future opportunities as well as challenges related to the use of deep learning in qMRI for target contouring, quantitative parameter estimation and also the generation of synthetic computerized tomography (CT) data based on MRI in personalized RT.Semin Radiat Oncol 32:377-388 (c) 2022 Elsevier Inc. All rights reserved.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1053-4296
• Sprache: Englisch