CMR and Computational Cardiology

CMR and computational cardiology involve the use of computational models and simulations to study the dynamics of the heart's electrical activity, mechanical function, and hemodynamics. These models help in understanding the underlying mechanisms of heart diseases, predicting treatment outcomes, and optimizing therapeutic strategies, thereby advancing the field of cardiology. The topics are electrocardiographic imaging (ECGi), artificial intelligence (AI), and MRI-compatible 12-lead ECG systems.

Electrocardiographic Imaging

ECGi is an advanced technique used in cardiology to non-invasively map the electrical activity of the heart. It combines data from body surface ECGs with imaging techniques such as MRI to create detailed 3D maps of cardiac electrical activity. ECGi helps in diagnosing and understanding various cardiac arrhythmias and can aid in planning interventions such as ablation therapy for restoring normal heart rhythm.

MR-compatible 12-lead ECG systems

As one of the few centers in the world, we have two MRI-compatible 12-lead ECG systems MiRTLE (IMRICOR) and Pelex-Max (PinMed Inc.) available. MRI-compatible 12-lead ECG systems are specialized devices designed to acquire high-quality ECG signals within the MRI environment. By integrating with MRI scanners, they enable simultaneous monitoring of cardiac electrical activity and imaging of cardiac structure and function. These systems are particularly useful for assessing cardiac function during MRI exams, facilitating the diagnosis of cardiac conditions such as arrhythmias, ischemia, and myocardial infarction. Additionally, they contribute to research efforts aimed at better understanding cardiac physiology and advancing cardiac imaging techniques like real-time ECGi.

Artificial Intelligence

AI plays a crucial role in cardiac computational modeling by leveraging machine learning algorithms and data-driven approaches to simulate and analyze cardiac function. AI techniques are used to integrate diverse data sources such as medical imaging, genetic information, and physiological measurements to create personalized models of cardiac activity. These models enable the prediction of various cardiac conditions, assist in treatment planning, and help optimize therapies for individual patients. By harnessing AI in cardiac computational modeling, researchers aim to enhance our understanding of cardiac physiology, improve diagnostics, and develop more effective treatments for cardiovascular diseases.