Deep learning in MRI-guided radiation therapy: A systematic review Review


Authors: Eidex, Z.; Ding, Y.; Wang, J.; Abouei, E.; Qiu, R. L. J.; Liu, T.; Wang, T.; Yang, X.
Review Title: Deep learning in MRI-guided radiation therapy: A systematic review
Abstract: Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x-ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi-modal, visual transformer, and diffusion models. © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.
Keywords: review; cancer radiotherapy; nuclear magnetic resonance imaging; radiotherapy; diffusion; systematic review; radiation therapy; motion; synthesis; x ray; image segmentation; clinical significance; prognosis; human; deep learning; radiomics; mri-guided
Journal Title: Journal of Applied Clinical Medical Physics
Volume: 25
Issue: 2
ISSN: 1526-9914
Publisher: American College of Medical Physics  
Date Published: 2024-02-01
Start Page: e14155
Language: English
DOI: 10.1002/acm2.14155
PUBMED: 37712893
PROVIDER: scopus
PMCID: PMC10860468
DOI/URL:
Notes: Review -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- Source: Scopus
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  1. Tonghe Wang
    51 Wang