Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology

Abstract

Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy.

A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction

“…Arrhythmia risk prediction in patients with MI and preserved ejection fraction previous findings of VT organizing centres in grey zone, 12,13,29,30 and experimental data which showed that the infarcted border served as the isthmus and a centrifugal pathway in different VT morphologies.…”

“…13,27,[29][30][31] Accurate reconstruction of ventricular geometry has been validated with ex-vivo and in-vivo imaging datasets. 12,32 The approach to construct a model of the iv64 infarcted ventricles by thresholding the infarct into only scar and (homogeneous) grey zone, as done in the present study, without accounting for additional small-scale heterogeneities in the grey zone, has been recently validated with experimental data. Specifically, Deng et al 13 used sock epicardial data for infarct-related VT, obtained from in-vivo swine heart, and demonstrated that ventricular models reconstructed from MRI data of the corresponding hearts were able to predict fairly accurately the morphology of each VT reentrant circuit and its organizing centre (e.g.…”

“…The MRI image stacks were segmented using our recently published segmentation and interpolation method, 12,13 an updated methodology in comparison to what was previously used in the VARP approach.…”

Optogenetic defibrillation terminates ventricular arrhythmia in mouse hearts and human simulations

“…Geometrical model of the diseased human ventricles. We used a computational model of the diseased human ventricles reconstructed from clinical late gadolinium-enhanced MRI (LGE-MRI) scans of a patient with post-myocardial infarction VT (33). The modeling approach has been previously validated in infarct-related VT inducibility studies (12,34,35).…”

Influence of Torso Model Complexity on the Noninvasive Localization of Ectopic Ventricular Activity

“…Precision of the reconstruction of the patient-specific geometry from CT images was studied by Prakosa in [19] and its influence on accurate electrophysiological imaging by Rahimi [20]. In this study specialized image-processing software TomoCon PACS® was used to construct precise patient-specific torso geometries from whole torso CT scans.…”