معرفی
Marina Strocchi is an active researcher specializing in computational cardiology and cardiac electrophysiology with a focus on cardiac digital twins, computational modeling, and cardiac resynchronization therapy. Her research contributes to UN Sustainable Development Goals related to health and wellbeing. With 66 citations across her publications, she maintains an active research profile with recent work published through 2025.
Her research interests center on cardiac electrophysiology and computational modeling, with particular expertise in Cardiac Resynchronization Therapy (100%), Bundle Branches (36%), Hemodynamics (30%), Magnetic Resonance Imaging (28%), Endocardium (26%), and Myocardium (19%). Her work bridges clinical cardiac applications with advanced computational techniques, focusing on improving cardiac pacing therapies and developing digital twin technology for cardiac applications.
Analysis of her recent publications reveals a strong trend toward large-scale cardiac modeling using MRI data, with increasing focus on population-based studies (evident in the 2025 UK Biobank paper with ~55,000 participants). Her research consistently integrates computational modeling with clinical cardiac electrophysiology, particularly examining different pacing modalities for cardiac resynchronization therapy. A notable theme across her work is the development of open tools and datasets to advance cardiac modeling research, as demonstrated by her publicly available virtual cohort of four-chamber heart meshes.
Dr. Strocchi maintains extensive collaborations across the cardiac research community, working with numerous researchers on projects spanning cardiac electrophysiology, computational modeling, and clinical applications. Her work appears to be supported by research funding that enables participation in large-scale projects like the UK Biobank initiative, though specific grant details are not provided in the available information.
Her research group appears to focus on developing computational frameworks for cardiac modeling, with particular emphasis on creating tools that bridge the gap between clinical cardiac applications and advanced computational techniques. The team seems to specialize in cardiac mesh generation, digital twin development, and analysis of cardiac pacing therapies using multimodal imaging approaches.