Cardiac Magnetic Resonance
Cardiac magnetic resonance (CMR) imaging is a gold-standard technique for non-invasive cardiac assessment, aiming to provide comprehensive information on heart structure and function. Current research heavily utilizes deep learning, employing architectures like U-Nets, Transformers, and diffusion models, to improve image reconstruction from undersampled data, automate segmentation of cardiac structures (especially the challenging right ventricle), and enable efficient biomarker quantification. These advancements enhance diagnostic accuracy, streamline clinical workflows, and facilitate personalized medicine by enabling more precise and efficient analysis of cardiac data, including the development of cardiac digital twins.
Papers
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
Carles Garcia-Cabrera, Eric Arazo, Kathleen M. Curran, Noel E. O'Connor, Kevin McGuinness
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
Amin Ranem, John Kalkhof, Caner Özer, Anirban Mukhopadhyay, Ilkay Oksuz
Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images
Dong Wei, Ying Sun, Ping Chai, Adrian Low, Sim Heng Ong
A Comprehensive 3-D Framework for Automatic Quantification of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images
Dong Wei, Ying Sun, Sim-Heng Ong, Ping Chai, Lynette L Teo, Adrian F Low