Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Poster

Investigating complex-valued neural networks applied to phase-cycled bSSFP for multi-parametric quantitative tissue characterization

MPG-Autoren
/persons/resource/persons260972

Birk,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons252833

Steiglechner,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84187

Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons216029

Heule,  R
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://archive.ismrm.org/2022/2532.html
(Zusammenfassung)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Birk, F., Steiglechner, J., Scheffler, K., & Heule, R. (2022). Investigating complex-valued neural networks applied to phase-cycled bSSFP for multi-parametric quantitative tissue characterization. Poster presented at Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022), London, UK.


Zitierlink: https://hdl.handle.net/21.11116/0000-000A-5C2F-C
Zusammenfassung
The bSSFP sequence is highly sensitive to relaxation parameters, tissue microstructure, and off-resonance frequencies, which has recently been shown to enable multi-parametric tissue characterization in the human brain using real-valued NNs. In this work, a new approach based on complex-valued NNs for voxel-wise simultaneous multi-parametric quantitative mapping with phase-cycled bSSFP input data is presented, possibly facilitating data handling. Relaxometry parameters (T1, T2) and field map estimates (B1+, ΔB0) could be quantified with high robustness and accuracy. The quantitative results were compared for different activation functions, favoring phase-sensitive implementations.