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Related Experiment Video

Updated: Jun 28, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Dynamic cardiac mapping on patient-specific cardiac models.

Kevin Wilson1, Gerard Guiraudon, Doug Jones

  • 1Imaging Research Laboratories, Robarts Research Institute, University of Western Ontario, Canada. kevin.wilson@vanderbilt.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 5, 2008
PubMed
Summary

This study introduces a dynamic cardiac mapping system that collects and displays electrophysiological data on patient-specific models. The novel system enhances cardiac mapping accuracy and provides a more comprehensive view for catheter ablation therapy.

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Last Updated: Jun 28, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

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Published on: April 11, 2025

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

  • Cardiovascular Electrophysiology
  • Medical Imaging
  • Computer-Aided Medical Interventions

Background:

  • Minimally invasive cardiac procedures rely on advanced computer-aided technologies for mapping and ablation.
  • Current cardiac mapping systems have limitations in temporal data display and patient-specific model integration.

Purpose of the Study:

  • To develop and validate a novel cardiac mapping system for collecting and displaying electrophysiological data in the temporal domain.
  • To integrate spatially tracked cardiac electrograms with patient-specific cardiac models for enhanced interpretation.
  • To create a more comprehensive cardiac mapping system compared to existing technologies.

Main Methods:

  • Development of novel approaches for collecting spatially tracked cardiac electrograms.
  • Registration of electrophysiological data with patient-specific cardiac models.
  • Laboratory validation of landmark navigation accuracy (physical and virtual).
  • In-vivo porcine experiment to assess real-time data collection and mapping onto a dynamic cardiac model.

Main Results:

  • The dynamic cardiac mapping system demonstrated high accuracy in locating physical and virtual landmarks.
  • The system successfully created dynamic cardiac maps displayed on dynamic cardiac surface models.
  • Spatially tracked electrophysiological data was effectively collected and mapped in an in-vivo setting.

Conclusions:

  • The developed dynamic cardiac mapping system offers improved accuracy and comprehensiveness for electrophysiological procedures.
  • This technology has the potential to enhance catheter ablation therapy by providing real-time, model-integrated data.
  • The system represents a significant advancement in computer-aided cardiac interventions.