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
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
Mahboobeh Jafari, Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi, Parisa Moridian, Niloufar Delfan, Roohallah Alizadehsani, Abbas Khosravi, Sai Ho Ling, Yu-Dong Zhang, Shui-Hua Wang, Juan M. Gorriz, Hamid Alinejad Rokny, U. Rajendra Acharya
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
Mahboobeh Jafari, Afshin Shoeibi, Navid Ghassemi, Jonathan Heras, Sai Ho Ling, Amin Beheshti, Yu-Dong Zhang, Shui-Hua Wang, Roohallah Alizadehsani, Juan M. Gorriz, U. Rajendra Acharya, Hamid Alinejad Rokny
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