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Review
. 2022 Oct;32(4):377-388.
doi: 10.1016/j.semradonc.2022.06.007.

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

Oliver J Gurney-Champion  1 , Guillaume Landry  2 , Kathrine Røe Redalen  3 , Daniela Thorwarth  4
Affiliations
Review

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

Oliver J Gurney-Champion et al. Semin Radiat Oncol. 2022 Oct.

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.

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Conflict of interest statement

Conflict of Interest O.J. Gurney-Champion has no conflicts of interest to declare. K.R. Redalen has no conflicts of interest to declare. D. Thorwarth declares institutional collaborations including financial and non-financial support from Elekta, Philips, TheraPanacea, Kaiku and PTW Freiburg. G.Landry: The Department of Radiation Oncology has research agreements with Viewray, Elekta and Brainlab.

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