Non-invasive imaging methods for detecting intratumoural viral spread and host responses to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents. Here we show that in mice with glioblastoma multiforme, the early apoptotic responses to oncolytic virotherapy (characterized by decreased cytosolic pH and reduced protein synthesis) can be rapidly detected via chemical-exchange-saturation-transfer magnetic resonance fingerprinting (CEST-MRF) aided by deep learning. By leveraging a deep neural network trained with simulated magnetic resonance fingerprints, CEST-MRF can generate quantitative maps of intratumoural pH and of protein and lipid concentrations by selectively labelling the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring exogenous contrast agents. We also show that in a healthy volunteer, CEST-MRF yielded molecular parameters that are in good agreement with values from the literature. Deep-learning-aided CEST-MRF may also be amenable to the characterization of host responses to other cancer therapies and to the detection of cardiac and neurological pathologies.
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Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals
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The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, but they are available for research purposes from the corresponding authors on reasonable request. Source data are provided with this paper.
CEST MR fingerprinting dictionaries were generated on the basis of previously published methods44,65 and used the following components: Matlab R2018a (Mathworks) and C++. These dictionaries can be reproduced using the open-source code available in https://pulseq-cest.github.io (ref. 89) with the parameters described in Supplementary Table 1. Conventional CEST analysis can be performed using the code available in https://github.com/cest-sources. The deep-learning models can be reproduced using standard libraries and scripts available in Python 3.6 and TensorFlow 1.4.1. All source code is available from the corresponding authors upon request.
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The work was supported by the US National Institutes of Health Grants R01-CA203873 (C.T.F.), P41-RR14075 (Massachusetts General Hospital), S10-RR023401 (Massachusetts General Hospital), S10-RR019307 (Massachusetts General Hospital) and 1S10RR023043 (Massachusetts General Hospital), as well as by R01-NS110942 (H.N., E.A.C.) and P01-CA163205 (H.N., E.A.C.). The Brigham and Women’s Small Animal Imaging Laboratory (SAIL) was funded by a G20 Grant (G20-RR031051) as part of the American Recovery and Reinvestment Act as part of the construction of the Brigham and Women’s MRI Research Center (BWMRC). The 7T Bruker Small Bore Animal Magnet was partially funded by an S10 Grant (S10-OD010705) through the National Institutes of Health. K.H was supported by the German Research Foundation (DFG, grant ZA 814/2–1). This project also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 836752 (OncoViroMRI). This paper reflects only the authors’ view, and the Research Executive Agency of the European Commission is not responsible for any use that may be made of the information it contains.
The authors declare the following competing interests: C.T.F., M.S.R. and O.C. hold a patent for the CEST MR fingerprinting method (patent no. US10,605,877).
Peer review information Nature Biomedical Engineering thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Perlman, O., Ito, H., Herz, K. et al. Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nat. Biomed. Eng 6, 648–657 (2022). https://doi.org/10.1038/s41551-021-00809-7