"Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for ..."

Elisabeth Hoppe et al. (2017)

> Home

Details and statistics

DOI: 10.3233/978-1-61499-808-2-202

access: open

type: Conference or Workshop Paper

metadata version: 2021-02-10

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - conf/gmds/HoppeKWWLPM17
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/gmds/HoppeKWWLPM17
Elisabeth Hoppe, Gregor Körzdörfer, Tobias Würfl, Jens Wetzl, Felix Lugauer, Josef Pfeuffer, Andreas K. Maier:
Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series. GMDS 2017: 202-206

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.