Electrocardiographic mapping (ECGI) detects reentrant drivers (RDs) that perpetuate arrhythmia in persistent AF (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) identify all latent sites in the fibrotic substrate that could potentially sustain RDs, not just those manifested during mapped AF. The objective of this study was to compare RDs from simulations and ECGI (RD/RD) and analyze implications for ablation. We considered 12 PsAF patients who underwent RD ablation. For the same cohort, we simulated AF and identified RD sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RD- and RD-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RD/RD were analyzed. RD were predicted in 28 atrial regions (median [inter-quartile range (IQR)] = 3.0 [1.0; 3.0] per model). ECGI detected 42 RD-harboring regions (4.0 [2.0; 5.0] per patient). The number of regions with RD and RD per individual was not significantly correlated ( = 0.46, = ns). The overall rate of regional agreement was fair (modified Cohen's κ statistic = 0.11), as expected, based on the different mechanistic underpinning of RD- and RD. nineteen regions were found to harbor both RD and RD, suggesting that a subset of clinically observed RDs was fibrosis-mediated. The most frequent source of differences (23/32 regions) between the two modalities was the presence of RD perpetuated by mechanisms other than the fibrotic substrate. In 6/12 patients, there was at least one region where a latent RD was observed in simulations but was not manifested during clinical mapping. Ablation of fibrosis-mediated RD (i.e., targets in regions that also harbored RD) trended toward a higher rate of positive response compared to ablation of other RD targets (57 vs. 41%, = ns). Our analysis suggests that RDs in human PsAF are at least partially fibrosis-mediated. Substrate-based ablation combining simulations with ECGI could improve outcomes.
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http://dx.doi.org/10.3389/fphys.2018.00414 | DOI Listing |
Heart Rhythm
December 2024
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Background: Electrocardiographic imaging (ECGi) is a noninvasive technique for ventricular tachycardia ablation planning. However, it is limited to reconstructing epicardial surface activation. In silico pace mapping combines a personalized computational model with clinical electrocardiograms (ECGs) to generate a virtual 3-dimensional pace map.
View Article and Find Full Text PDFComput Biol Med
November 2024
COR Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain; Corify Care SL, Madrid, Spain.
Background: In electrocardiographic imaging (ECGI), selecting an optimal regularization parameter (λ) is crucial for obtaining accurate inverse electrograms. The effects of signal and geometry uncertainties on the inverse problem regularization have not been thoroughly quantified, and there is no established methodology to identify when λ is sub-optimal due to these uncertainties. This study introduces a novel approach to λ selection using Tikhonov regularization and L-curve optimization, specifically addressing the impact of electrical noise in body surface potential map (BSPM) signals and geometrical inaccuracies in the cardiac mesh.
View Article and Find Full Text PDFmedRxiv
November 2024
College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA.
Background: Studies of VT mechanisms are largely based on a 2D portrait of reentrant circuits on one surface of the heart. This oversimplifies the 3D circuit that involves the depth of the myocardium. Simultaneous epicardial and endocardial (epi-endo) mapping was shown to facilitate a 3D delineation of VT circuits, which is however difficult via invasive mapping.
View Article and Find Full Text PDFState-space modeling (SSM) provides a general framework for many image reconstruction tasks. Error in a priori physiological knowledge of the imaging physics, can bring incorrectness to solutions. Modern deep-learning approaches show great promise but lack interpretability and rely on large amounts of labeled data.
View Article and Find Full Text PDFAdv Radiat Oncol
May 2023
Department of Radiation Oncology, New York University Grossman School of Medicine, New York, New York.
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