Search Results for author:
Found 7 papers, 4 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).
Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI
First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease.
CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images
External validation of MVT (MVTexternal) was performed on data from 3 previously unseen magnet systems from 2 vendors (n=80 patients).
Physics-informed neural networks for myocardial perfusion MRI quantification
This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters.
Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation
Deep learning has shown the potential to automate the requisite cardiac structure segmentation.
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.
