4 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…