English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

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

Perlman, O., Ito, H., Herz, K., Shono, N., Nakashima, H., Zaiss, M., et al. (2021). Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nature Biomedical Engineering, Epub ahead. doi:10.1038/s41551-021-00809-7.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Perlman, O, Author
Ito, H, Author
Herz, K1, 2, Author              
Shono, N, Author
Nakashima, H, Author
Zaiss, M1, 2, Author              
Chiocca, EA, Author
Cohen, O, Author
Rosen, MS, Author
Farrar, CT, Author
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s):
 Dates: 2021-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41551-021-00809-7
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Nature Biomedical Engineering
  Abbreviation : Nat Biomed Eng
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London ; New York NY ; Tokyo : Nature Research
Pages: - Volume / Issue: Epub ahead Sequence Number: - Start / End Page: - Identifier: ISSN: 2157-846X
CoNE: https://pure.mpg.de/cone/journals/resource/2157-846X