Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning

Or Perlman*, Hirotaka Ito, Kai Herz, Naoyuki Shono, Hiroshi Nakashima, Moritz Zaiss, E. Antonio Chiocca, Ouri Cohen, Matthew S. Rosen, Christian T. Farrar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)648-657
Number of pages10
JournalNature Biomedical Engineering
Issue number5
StatePublished - May 2022
Externally publishedYes


FundersFunder number
Brigham and Women’s MRI Research CenterS10-OD010705
Marie Skłodowska-Curie
National Institutes of HealthP41-RR14075, R01-CA203873
National Cancer InstituteP01CA163205
California Department of Fish and Game
Massachusetts General HospitalR01-NS110942, S10-RR023401, 1S10RR023043, G20-RR031051, S10-RR019307, P01-CA163205
Horizon 2020 Framework Programme
H2020 Marie Skłodowska-Curie Actions836752
European Commission
Deutsche ForschungsgemeinschaftZA 814/2– 1


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