86 citations · 126 across the 12 of their papers we have counts for
10 papers · 1 filter
Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification
Christos Tsepas, Chang Yan, Maximilian Fuetterer +2
Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the dynamic contrast-enhanced MR data. However, fitting t…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation
Didier R. P. R. M. Lustermans, Sina Amirrajab, Mitko Veta +2
Background: The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tes…
CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images
Ruth P Lim, Stefan Kachel, Adriana DM Villa +5
Objectives: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficien…
Automated quantitative analysis of first-pass myocardial perfusion magnetic resonance imaging data
Cian M Scannell
Coronary artery disease (CAD) remains the world's leading cause of mortality and the disease burden is continually expanding as the population ages. Recently, the MR-INFORM randomi…
Physics-informed neural networks for myocardial perfusion MRI quantification
Rudolf L. M. van Herten, Amedeo Chiribiri, Marcel Breeuwer +2
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data…
Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information
Ana Lourenço, Eric Kerfoot, Irina Grigorescu +3
Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium…
Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation
Cian M. Scannell, Amedeo Chiribiri, Mitko Veta
Cine cardiac magnetic resonance (CMR) has become the gold standard for the non-invasive evaluation of cardiac function. In particular, it allows the accurate quantification of func…
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI
Cian M. Scannell, Piet van den Bosch, Amedeo Chiribiri +3
The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia. However, due to the relativel…
Hierarchical Bayesian myocardial perfusion quantification
Cian M. Scannell, Amedeo Chiribiri, Adriana D. M. Villa +2
Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data. However, the model-fitting can produce unreliable results due to the limit…