Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications.

Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.

An Analysis of Open-Ended Coaxial Probe Sensitivity to Heterogeneous Media.

Open-ended coaxial probe spectroscopy is commonly used to determine the dielectric permittivity of biological tissues. However, heterogeneities in the probe sensing region can limit measurement precision and reproducibility. This study presents an analysis of the coaxial probe sensing region to elucidate the effects of heterogeneities on measured permittivity. Coaxial probe spectroscopy at 0.5-20 GHz was numerically simulated while a homogenous background was perturbed with a small inclusion of contrasting permittivity. Shifts in the measured effective permittivity provided a three-dimensional assessment of the probe sensitivity field. Sensitivity was well-approximated by the square of the electric field for each analyzed probe. Smaller probes were more sensitive to heterogeneities throughout their sensing region, but were less sensitive to spectral effects compared to larger probes. The probe sensing diameter was less than 0.5 mm in all directions by multiple metrics. Therefore, small heterogeneities may substantially impact permittivity measurement in biological tissues if located near the probe-tissue interface.

Computed Tomography-Based Modeling of Water Vapor-Induced Changes in Permittivity During Microwave Ablation.

OBJECTIVE: Measurements of tissue permittivity with small open-ended coaxial probes during microwave tissue heating have been plagued by high variability as tissue water becomes vaporized. Analysis of such variability has been hampered by a lack of direct visualization of the measurement volume. The objective of this study was to determine if X-ray computed tomography (CT) could be used to visualize the measurement volume and then predict dielectric permittivity based on the visualized tissue composition. METHODS: CT attenuation at 120 kVp was measured at the end of an open-ended coaxial probe during microwave ablation of ex vivo liver (2.45 GHz at 50 W delivered for 5 minutes). Tissue composition was estimated from attenuation maps and used to predict dielectric properties based on established mixture equations. Predicted permittivity was then compared to measured values using error metrics and linear regression. RESULTS: There was a good agreement between measured and modeled permittivity during 5-minute ablations at 2.45 GHz (r = 0.94, p < .001). Normalized root mean squared errors were below 17% in permittivity modeling at 2.45 GHz. CONCLUSION AND SIGNIFICANCE: CT-based model predictions of dielectric properties are feasible in ex vivo liver. The models may facilitate real-time imaging-based permittivity mapping.

Development of Water Content Dependent Tissue Dielectric Property Models.

We propose dielectric tissue property models dependent on both water and air content covering the microwave frequency range. Water is the largest constituent of biological tissues and its effect on the dielectric properties of biological tissue has been studied. However, dehydration effects due to thermal heating have not been fully characterized. We combined 1) Maxwell-Fricke mixture theory with a four-pole Cole-Cole equation to include water and air content dependency and as the second approach a different 2) Maxwell mixture model was coupled with a Debye function. The proposed approaches (1 and 2) were able to predict the permittivity (ε') and conductivity (σ) of bovine liver and swine lung tissues at different hydration and inflation states from 1-15 GHz. A second approach coupling Maxwell and Debye models required fewer assumptions and modelled tissue properties with higher accuracy (less than 15% mean percent error in all tissue types). These models may help improve the accuracy of microwave ablation simulation when tissue water content changes as a result of vaporization, and may facilitate personalized treatment planning.

Quantifying optical properties with visible and near-infrared optical coherence tomography to visualize esophageal microwave ablation zones.

Microwave ablation is a minimally invasive image guided thermal therapy for cancer that can be adapted to endoscope use in the gastrointestinal (GI) tract. Microwave ablation in the GI tract requires precise control over the ablation zone that could be guided by high resolution imaging with quantitative contrast. Optical coherence tomography (OCT) provides ideal imaging resolution and allows for the quantification of tissue scattering properties to characterize ablated tissue. Visible and near-infrared OCT image analysis demonstrated increased scattering coefficients (µs ) in ablated versus normal tissues (Vis: 347.8%, NIR: 415.0%) and shows the potential for both wavelength ranges to provide quantitative contrast. These data suggest OCT could provide quantitative image guidance and valuable information about antenna performance in vivo.