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Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization.

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This study quantifies how variations in heart segmentation impact Electrocardiographic Imaging (ECGI). Preliminary results show minor changes in activation maps but no significant effect on pinpointing the source of cardiac electrical activity.

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Area of Science:

  • Biomedical Engineering
  • Computational Cardiology
  • Medical Imaging

Background:

  • Electrocardiographic Imaging (ECGI) non-invasively maps cardiac electrical activity using body surface potentials and patient-specific anatomy.
  • Accurate segmentation of heart and torso geometries is a critical, yet operator-dependent, first step in ECGI.
  • The impact of segmentation variability on ECGI accuracy remains unquantified.

Purpose of the Study:

  • To investigate the influence of segmentation variability on Electrocardiographic Imaging (ECGI) estimations.
  • To quantify the effect of inter-operator variations in cardiac segmentation on ECGI results.
  • To assess the impact of segmentation uncertainty on the reconstruction of cardiac electrical activity.

Main Methods:

  • Generated 262 shape models from fifteen distinct segmentations to simulate variability.
  • Designed test cases with and without shape model uncertainty.
  • Utilized four ectopic ventricular excitation cases to compare ECGI outcomes.

Main Results:

  • Preliminary analysis revealed minor variations in cardiac activation maps due to model uncertainty.
  • No significant impact was observed on the localization of excitation sources.
  • Segmentation variability showed a limited effect on the overall accuracy of ECGI source localization.

Conclusions:

  • Segmentation variability introduces minor perturbations in cardiac activation maps during ECGI.
  • The localization of cardiac electrical activity sources in ECGI is robust to segmentation uncertainty.
  • Further research may refine segmentation techniques to optimize ECGI precision.