IntroductionPulsed field ablation (PFA) exploits the delivery of short high-voltage shocks to induce cells death via irreversible electroporation. The therapy offers a potential paradigm shift for catheter ablation of cardiac arrhythmia. We designed an AC-burst generator and therapeutic strategy, based on the existing knowledge between efficacy and safety among different pulses. We performed a proof-of-concept chronic animal trial to test the feasibility and safety of our method and technology.MethodsWe employed 6 female swine – weight 53.75 ± 4.77 kg – in this study. With fluoroscopic and electroanatomical mapping assistance, we performed ECG-gated AC-PFA in the following settings: in the left atrium with a decapolar loop catheter with electrodes connected in bipolar fashion; across the interventricular septum applying energy between the distal electrodes of two tip catheters. After procedure and 4-week follow-up, the animals were euthanized, and the hearts were inspected for tissue changes and characterized. We perform finite element method simulation of our AC-PFA scenarios to corroborate our method and better interpret our findings.ResultsWe applied square, 50% duty cycle, AC bursts of 100 μs duration, 100 kHz internal frequency, 900 V for 60 pulses in the atrium and 1500 V for 120 pulses in the septum. The inter-burst interval was determined by the native heart rhythm – 69 ± 9 bpm. Acute changes in the atrial and ventricular electrograms were immediately visible at the sites of AC-PFA – signals were elongated and reduced in amplitude (p < 0.0001) and tissue impedance dropped (p = 0.011). No adverse event (e.g., esophageal temperature rises or gas bubble streams) was observed – while twitching was avoided by addition of electrosurgical return electrodes. The implemented numerical simulations confirmed the non-thermal nature of our AC-PFA and provided specific information on the estimated treated area and need of pulse trains. The postmortem chest inspection showed no peripheral damage, but epicardial and endocardial discolorations at sites of ablation. T1-weighted scans revealed specific tissue changes in atria and ventricles, confirmed to be fibrotic scars via trichrome staining. We found isolated, transmural and continuous scars. A surviving cardiomyocyte core was visible in basal ventricular lesions.ConclusionWe proved that our method and technology of AC-PFA is feasible and safe for atrial and ventricular myocardial ablation, supporting their systematic investigation into effectiveness evaluation for the treatment of cardiac arrhythmia. Further optimization, with energy titration or longer follow-up, is required for a robust atrial and ventricular AC-PFA.

Purpose A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning–based FPC. Methods Two novel deep learning–based FPC methods (deep learning–based Cr referencing and deep learning–based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA‐edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo study was carried out to investigate the performance of our proposed methods. Results The validation using low‐SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably with other methods. The evaluation showed that the deep learning–based spectral registration over a limited frequency range method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA‐edited in vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. Conclusions The proposed physics‐informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of large MRS data in a shorter time.

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Purpose: A supervised deep learning (DL) approach for frequency-and-phase Correction (FPC) of MR spectroscopy (MRS) data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised DL-based FPC. Method: Two novel DL-based FPC methods (deep learning-based Cr referencing [dCrR] and deep learning-based spectral registration [dSR]) which use a priori physics domain knowledge are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in-vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared to other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo (MC) study was carried out to investigate the performance of our proposed methods. Result: The validation using low-SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably to other methods. The evaluation showed that the dCrR method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA-edited in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. Conclusion: The proposed physics-informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of MRS data in a shorter time.