Non-invasive delineation of ventricular tachycardia substrates for cardiac stereotactic body radiotherapy: utility of in-silico pace-mapping. (19th May 2022)

• Record Type: Journal Article
• Authors: Monaci, S; Qian, S; Gillette, K; Mukherjee, R; Haberland, U; Elliott, MK; Rajani, R; Rinaldi, CA; O'neill, M; Plank, G; King, A; Bishop, MJ
• Is Part Of: Europace. Volume 24:Supplement 1(2022)
• Publication Date: 2022-05-19
• Subjects: Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euac053.342
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: Public Institution(s). Main funding source(s): EPSRC Background: Cardiac stereotactive body radiotherapy (CSBRT) is an emerging, non-invasive ablation modality that targets ventricular tachycardia (VT) substrates in patients with limited conventional treatment options. Success of CSBRT hinges primarily on the correct identification of VT targets, which requires non-invasive planning. Current non-invasive, pre-procedure strategies employ multi-electrode electrocardiographic imaging (ECGi). Given its significant cost and potential challenges in detecting endocardial, intramural and/or septal VT sites, there is a need to optimise VT delineation strategies for CSBRT; patient-specific simulations may show promise at guiding such planning non-invasively. Purpose: We aim to perform non-invasive, in-silico pace-mapping on an image-based computational model to identify VT substrates for CSBRT. We intend to show the utility of our fast computational pipeline - relying on CT imaging data only - to provide further insights on inaccessible, scar-related VT episodes. Methods: A detailed computational torso model of a CSBRT candidate with incessant VT was generated from CT imaging data. Extracellular content volumes (ECVs) were used to identify different tissue types (healthy, border zone and non-conducting), and scale model tissue conductivities accordingly. In-silico pace-mapping was performed by simulating ~360 paced beats across the LV, and computing corresponding 12-lead ECGs within a fast electrophysiological (EP) simulation environment combining reaction-eikonal and lead field methods. QRS complexes from simulated paced beats were used to construct the virtual correlation pace-map against the measured QRS of the clinically-induced VT, along with a 'reference-less' virtual pace-map constructed from neighbouring paced-beat QRSs (within a 20 mm radius). An epicardial activation map of the clinically-induced VT was reconstructed from ECGi measurement, and used for comparison against our virtual pace-maps. Results: Correlations between simulated paced-beat QRS complexes and the clinically-induced VT QRS were higher in mid-apical, infero-septal segments - segment 9 (85.71%), 10 (87.95%) and 15 (89.58%) - identifying septal origin and pathway of the induced re-entrant circuit. A possible septal VT isthmus was also identified by a high gradient in the virtual reference-less pace-map in segment 9 (> 2.5%/mm). Our in-silico predictions were in agreement with the clinical regions identified for CSBRT (segment 9 and 15), and provided additional information on the 3D and septal dynamics of the VT episode. Conclusions: Our in-silico pace-mapping study successfully localised VT substrates in a patient unable to receive standard ablative procedures, and provided further clinical insight on the induced VT dynamics. Our rapid in-silico pace-mapping approach may be utilised to support optimal identification of VT target volumes for CSBRT.