Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.
Maurice PradellaMichael B ScottMuhammad OmerSeth K HillLisette LockhartXin YiAlborz Amir-KhaliliAlireza SojoudiBradley D AllenRyan AveryMichael MarklPublished in: European radiology (2022)
• Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases. • Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location. • The evaluated tool indicates usability in daily practice.
Keyphrases
- deep learning
- pulmonary artery
- coronary artery
- pulmonary hypertension
- pulmonary arterial hypertension
- contrast enhanced
- artificial intelligence
- convolutional neural network
- endothelial cells
- machine learning
- magnetic resonance imaging
- primary care
- healthcare
- magnetic resonance
- weight loss
- electronic health record
- physical activity
- loop mediated isothermal amplification
- computed tomography
- pluripotent stem cells
- clinical practice
- diffusion weighted imaging
- label free
- social media
- single cell