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Found 4 papers, 1 papers with code
High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D CINE MRI and Unsupervised Neural Networks
We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD).
Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training.
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI
The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia.
Hierarchical Bayesian myocardial perfusion quantification
Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data.
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