86 citations · 126 across the 12 of their papers we have counts for
13 papers
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…
Foundation Models Adaptation for Multi-View Multi-modal Cardiac MRI Segmentation and Direct Ejection Fraction Estimation
Sina Amirrajab, Cian M Scannell, Volker Vehof +2
Foundation models have shown strong transferability in cardiac MRI (CMR), but their effectiveness for heterogeneous multi-view and multi-sequence CMR analysis remains unclear. In t…
Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance
Noortje I. P. Schueler, Nathan C. K. Wong, Richard J. Crawley +3
Background: Quantitative stress perfusion cardiovascular magnetic resonance (CMR) is a powerful tool for assessing myocardial ischemia. Motion correction is essential for accurate…
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…
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine Segmentation
Rob A. J. de Mooij, Josien P. W. Pluim, Cian M. Scannell
Self-supervised pretraining (SSP) has shown promising results in learning from large unlabeled datasets and, thus, could be useful for automated cardiovascular magnetic resonance (…
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…