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<p>Electrical and structural remodeling of the heart can contribute to the development of cardiac arrhythmias. Ex vivo optical mapping has been used to visualize cardiac electrophysiological properties, activation and phase maps to further elucidate the mechanisms of atrial fibrillation and ventricular fibrillation initiation and persistence. Here we show an epicardial three-dimensional panoramic optical mapping tool integrated with micro-computed tomography automatically segmented with a deep learning model relying on a convolutional neural network to provide structural and electrical activation information in a single three-dimensional volume of a mouse heart. This technique allows for the acquisition and analysis of electrical activity of the entire epicardial surface with submillimeter spatial resolution and a temporal resolution of 1 ms. We establish the use of this method in transgenic mouse hearts with spontaneous atrial fibrillation and ventricular fibrillation, and mouse surgical models of myocardial infarction and left ventricular hypertrophy.</p>

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Three-dimensional visualization of arrhythmogenic substrate in mouse hearts using panoramic optical mapping and micro-computed tomography

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Lea Melki,
  • Uma Mahesh R. Avula,
  • Pavithran Guttipatti,
  • Ruiping Ji,
  • Najla Saadallah,
  • Muhammad Shaher Yar,
  • Jonah A. Majumder,
  • Albert Fang,
  • Amar Desai,
  • Naoko Yamaguchi,
  • David S. Park,
  • Ashwin Viswanathan,
  • Karen Conboy,
  • Brian Gill,
  • Christine P. Hendon,
  • Elaine Y. Wan

摘要

Electrical and structural remodeling of the heart can contribute to the development of cardiac arrhythmias. Ex vivo optical mapping has been used to visualize cardiac electrophysiological properties, activation and phase maps to further elucidate the mechanisms of atrial fibrillation and ventricular fibrillation initiation and persistence. Here we show an epicardial three-dimensional panoramic optical mapping tool integrated with micro-computed tomography automatically segmented with a deep learning model relying on a convolutional neural network to provide structural and electrical activation information in a single three-dimensional volume of a mouse heart. This technique allows for the acquisition and analysis of electrical activity of the entire epicardial surface with submillimeter spatial resolution and a temporal resolution of 1 ms. We establish the use of this method in transgenic mouse hearts with spontaneous atrial fibrillation and ventricular fibrillation, and mouse surgical models of myocardial infarction and left ventricular hypertrophy.