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Conference Papers Year : 2022

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

Abstract

Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fullyautomated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed.

Domains

Imaging
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Dates and versions

hal-03936058 , version 1 (12-01-2023)

Identifiers

  • HAL Id : hal-03936058 , version 1

Cite

Narimane Gassa, Machteld Boonstra, Jana Svehlikova, Dana Brooks, Akil Narayan, et al.. Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization. Computing in Cardiology 2022, Sep 2022, Tampere, Finland. ⟨hal-03936058⟩
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