Deep Learning in MRI-guided Radiation Therapy: A Systematic Review

MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning app...

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Autores principales: Eidex, Zach, Ding, Yifu, Wang, Jing, Abouei, Elham, Qiu, Richard L.J., Liu, Tian, Wang, Tonghe, Yang, Xiaofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055493/
https://www.ncbi.nlm.nih.gov/pubmed/36994167
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author Eidex, Zach
Ding, Yifu
Wang, Jing
Abouei, Elham
Qiu, Richard L.J.
Liu, Tian
Wang, Tonghe
Yang, Xiaofeng
author_facet Eidex, Zach
Ding, Yifu
Wang, Jing
Abouei, Elham
Qiu, Richard L.J.
Liu, Tian
Wang, Tonghe
Yang, Xiaofeng
author_sort Eidex, Zach
collection PubMed
description MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.
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spelling pubmed-100554932023-03-30 Deep Learning in MRI-guided Radiation Therapy: A Systematic Review Eidex, Zach Ding, Yifu Wang, Jing Abouei, Elham Qiu, Richard L.J. Liu, Tian Wang, Tonghe Yang, Xiaofeng ArXiv Article MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed. Cornell University 2023-03-30 /pmc/articles/PMC10055493/ /pubmed/36994167 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Eidex, Zach
Ding, Yifu
Wang, Jing
Abouei, Elham
Qiu, Richard L.J.
Liu, Tian
Wang, Tonghe
Yang, Xiaofeng
Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
title Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
title_full Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
title_fullStr Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
title_full_unstemmed Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
title_short Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
title_sort deep learning in mri-guided radiation therapy: a systematic review
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055493/
https://www.ncbi.nlm.nih.gov/pubmed/36994167
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