Cardiovascular imaging techniques for electrophysiologists.

May 13, 2025pubmed logopapers

Authors

Rogers AJ,Reynbakh O,Ahmed A,Chung MK,Charate R,Yarmohammadi H,Gopinathannair R,Khan H,Lakkireddy D,Leal M,Srivatsa U,Trayanova N,Wan EY

Affiliations (9)

  • Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
  • Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Kansas City Heart Rhythm Institute and Research Foundation, Overland Park, KS, USA.
  • Heart, Vascular and Thoracic Institute and Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Norton Heart Specialists, Norton Healthcare, Louisville, KY, USA.
  • Division of Cardiology, Department of Medicine, Emory University, Atlanta, GA, USA.
  • Division of Cardiovascular Medicine, University of California Davis Medical Center, Davis, CA, USA.
  • Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University Baltimore, Baltimore, MD, USA.
  • Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA. [email protected].

Abstract

Rapid technological advancements in noninvasive and invasive imaging including echocardiography, computed tomography, magnetic resonance imaging and positron emission tomography have allowed for improved anatomical visualization and precise measurement of cardiac structure and function. These imaging modalities allow for evaluation of how cardiac substrate changes, such as myocardial wall thickness, fibrosis, scarring and chamber enlargement and/or dilation, have an important role in arrhythmia initiation and perpetuation. Here, we review the various imaging techniques and modalities used by clinical and basic electrophysiologists to study cardiac arrhythmia mechanisms, periprocedural planning, risk stratification and precise delivery of ablation therapy. We also review the use of artificial intelligence and machine learning to improve identification of areas for triggered activity and isthmuses in reentrant arrhythmias, which may be favorable ablation targets.

Topics

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