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Background: Cardiac optical mapping is an imaging technique that measures fluorescent signals across a cardiac preparation. Dual optical imaging of voltage-sensitive and calcium-sensitive probes allows for simultaneous recordings of cardiac action potentials and intracellular calcium transients with high spatiotemporal resolution. The analysis of these complex optical datasets is both time intensive and technically challenging; as such, we have developed a software package for semi-automated image processing and analysis. Herein, we report an updated version of our software package (KairoSight-3.0) with features to enhance the characterization of cardiac parameters using optical signals. Methods: To test software validity and applicability, we used Langendorff-perfused heart preparations to record transmembrane voltage and intracellular calcium signals from the epicardial surface. Isolated hearts from guinea pigs and rats were loaded with a potentiometric dye (RH237) and/or calcium indicator dye (Rhod-2AM) and fluorescent signals were acquired. We used Python 3.8.5 programming language to develop the KairoSight-3.0 software. Cardiac maps were validated with a user-specified manual mapping approach. Results: Manual maps of action potential duration (30 or 80 % repolarization), calcium transient duration (30 or 80 % reuptake), action potential and calcium transient alternans were constituted to validate the accuracy of software-generated maps. Manual and software maps had high accuracy, with >97 % of manual and software values falling within 10 ms of each other and >75 % within 5 ms for action potential duration and calcium transient duration measurements (n = 1000–2000 pixels). Further, our software package includes additional measurement tools to analyze signal-to-noise ratio, conduction velocity, action potential and calcium transient alternans, and action potential-calcium transient coupling time to produce physiologically meaningful optical maps. Conclusions: KairoSight-3.0 has enhanced capabilities to perform measurements of cardiac electrophysiology, calcium handling, alternans, and the excitation-contraction coupling with satisfactory accuracy.

Electrocardiograms (ECG) are universally used to measure the electrical activity of the heart; however, variations in recording techniques and/or subject demographics can affect ECG interpretation. In this study, we investigated variables that are likely to influence ECG metric measurements in cardiovascular research, including recording technique, use of anesthesia, and animal model characteristics. Awake limb lead ECG recordings were collected in vivo from adult guinea pigs using a platform ECG system, while recordings in anesthetized animals were performed using both a platform and needle ECG system. We report significant heterogeneities in ECG metric values that are attributed to methodological differences (e.g., ECG lead configuration, ECG recording platform, presence or absence of anesthesia) that persist even within the same cohort of animals. Further, we report that variability in animal demographics is preserved in vivo ECG recordings—with animal age serving as a significant contributor, while sex-specific influences were less pronounced. Methodological approaches and subject demographics should be fully considered when interpreting ECG values in animal models, comparing datasets between studies, or developing artificial intelligence algorithms that utilize an ECG database.

Cardiac optical mapping, also known as optocardiography, employs parameter-sensitive fluorescence dye(s) to image cardiac tissue and resolve the electrical and calcium oscillations that underly cardiac function. This technique is increasingly being used in conjunction with, or even as a replacement for, traditional electrocardiography. Over the last several decades, optical mapping has matured into a “gold standard” for cardiac research applications, yet the analysis of optical signals can be challenging. Despite the refinement of software tools and algorithms, significant programming expertise is often required to analyze large optical data sets, and data analysis can be laborious and time-consuming. To address this challenge, we developed an accessible, open-source software script that is untethered from any subscription-based programming language. The described software, written in python, is aptly named “KairoSight” in reference to the Greek word for “opportune time” (Kairos) and the ability to “see” voltage and calcium signals acquired from cardiac tissue. To demonstrate analysis features and highlight species differences, we employed experimental datasets collected from mammalian hearts (Langendorff-perfused rat, guinea pig, and swine) dyed with RH237 (transmembrane voltage) and Rhod-2, AM (intracellular calcium), as well as human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) dyed with FluoVolt (membrane potential), and Fluo-4, AM (calcium indicator). We also demonstrate cardiac responsiveness to ryanodine (ryanodine receptor modulator) and isoproterenol (beta-adrenergic agonist) and highlight regional differences after an ablation injury. KairoSight can be employed by both basic and clinical scientists to analyze complex cardiac optical mapping datasets without requiring dedicated computer science expertise or proprietary software.

Currently, there are limited means for high-resolution monitoring of tissue injury during radiofrequency ablation procedures.To develop the next generation of visualization catheters that can reveal irreversible atrial muscle damage caused by ablation and identify viability gaps between the lesions.Radiofrequency lesions were placed on the endocardial surfaces of excised human and bovine atria and left ventricles of blood perfused rat hearts. Tissue was illuminated with 365nm light and a series of images were acquired from individual spectral bands within 420-720nm range. By extracting spectral profiles of individual pixels and spectral unmixing, the relative contribution of ablated and unablated spectra to each pixel was then displayed. Results of spectral unmixing were compared to lesion pathology.RF ablation caused significant changes in the tissue autofluorescence profile. The magnitude of these spectral changes in human left atrium was relatively small (< 10% of peak fluorescence value), yet highly significant. Spectral unmixing of hyperspectral datasets enabled high spatial resolution, in-situ delineation of radiofrequency lesion boundaries without the need for exogenous markers. Lesion dimensions derived from hyperspectral imaging approach strongly correlated with histological outcomes. Presence of blood within the myocardium decreased the amplitude of the autofluorescence spectra while having minimal effect on their overall shapes. As a result, the ability of hyperspectral imaging to delineate ablation lesions in vivo was not affected.Hyperspectral imaging greatly increases the contrast between ablated and unablated tissue enabling visualization of viability gaps at clinically relevant locations. Data supports the possibility for developing percutaneous hyperspectral catheters for high-resolution ablation guidance.