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Abstract: RinQ Fingerprinting

Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting
  • Elisabeth HoppeEmail author
  • Florian ThammEmail author
  • Gregor Körzdörfer
  • Christopher Syben
  • Franziska Schirrmacher
  • Mathias Nittka
  • Josef Pfeuffer
  • Heiko Meyer
  • Andreas Maier
Conference paper
  • 56 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network.

Literatur

  1. 1.
    Hoppe E, Thamm F, Körzdörfer G, et al. RinQ fingerprinting: recurrence-informed quantile networks for magnetic resonance fingerprinting. In: Proc MICCAI. Springer; 2019. p. 92–100.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Elisabeth Hoppe
    • 1
    Email author
  • Florian Thamm
    • 1
    Email author
  • Gregor Körzdörfer
    • 2
  • Christopher Syben
    • 1
  • Franziska Schirrmacher
    • 1
  • Mathias Nittka
    • 2
  • Josef Pfeuffer
    • 2
  • Heiko Meyer
    • 2
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition Lab, Department of Computer ScienceFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.MR Application DevelopmentSiemens HealthcareErlangenDeutschland

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