MOCOnet: robust motion correction of cardiovascular magnetic resonance T1 mapping using convolutional neural networks

Geahča maid

• Fast and robust motion correction of cardiovascular magnetic resonance T1-mapping using data-driven convolutional neural networks for generalisability
  Dahkki: Gonzales, RA, et al.
  Almmustuhtton: (2022)
• Development of deep learning virtual native enhancement for gadolinium-free myocardial infarction and viability assessment
  Dahkki: Zhang, Q, et al.
  Almmustuhtton: (2022)
• Ensemble of deep convolutional neural networks with Monte Carlo dropout sampling for automated image segmentation quality control and robust deep learning using small datasets
  Dahkki: Hann, E, et al.
  Almmustuhtton: (2021)
• Standardized image post-processing of cardiovascular magnetic resonance T1-mapping reduces variability and improves accuracy and consistency in myocardial tissue characterization
  Dahkki: Carapella, V, et al.
  Almmustuhtton: (2019)
• Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy
  Dahkki: Zhang, Q, et al.
  Almmustuhtton: (2021)