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Improving accuracy of myocardial T<sub>1</sub> estimation in MyoMapNet.

Rui GuoZhensen ChenAmine AmyarHossam El-RewaidySalah AssanaJennifer RodriguezPatrick PierceBeth GodduReza Nezafat
Published in: Magnetic resonance in medicine (2022)
Training MyoMapNet with numerical simulations and phantom data will improve the estimation of myocardial T<sub>1</sub> values and increase its robustness to confounders while also reducing the overall T<sub>1</sub> mapping estimation time to only 4 heartbeats.
Keyphrases
  • left ventricular
  • high resolution
  • electronic health record
  • molecular dynamics
  • magnetic resonance imaging
  • heart failure
  • machine learning
  • big data
  • magnetic resonance
  • image quality
  • atrial fibrillation