Fluorescence optical imaging techniques have revolutionized the field of cardiac electrophysiology and advanced our understanding of complex electrical activities such as arrhythmias. However, traditional monocular optical mapping systems, despite having high spatial resolution, are restricted to a two-dimensional (2D) field of view. Consequently, tracking complex three-dimensional (3D) electrical waves such as during ventricular fibrillation is challenging as the waves rapidly move in and out of the field of view. This problem has been solved by panoramic imaging which uses multiple cameras to measure the electrical activity from the entire epicardial surface. However, the diverse engineering skill set and substantial resource cost required to design and implement this solution have made it largely inaccessible to the biomedical research community at large. To address this barrier to entry, we present an open source toolkit for building panoramic optical mapping systems which includes the 3D printing of perfusion and imaging hardware, as well as software for data processing and analysis. In this paper, we describe the toolkit and demonstrate it on different mammalian hearts: mouse, rat, and rabbit.

Since the 1970s fluorescence imaging has become a leading tool in the discovery of mechanisms of cardiac function and arrhythmias. Gradual improvements in fluorescent probes and multi-camera technology have increased the power of optical mapping and made a major impact on the field of cardiac electrophysiology. Tandem-lens optical mapping systems facilitated simultaneous recording of multiple parameters characterizing cardiac function. However, high cost and technological complexity restricted its proliferation to the wider biological community. We present here, an open-source solution for multiple-camera tandem-lens optical systems for multiparametric mapping of transmembrane potential, intracellular calcium dynamics and other parameters in intact mouse hearts and in rat heart slices. This 3D-printable hardware and Matlab-based RHYTHM 1.2 analysis software are distributed under an MIT open-source license. Rapid prototyping permits the development of inexpensive, customized systems with broad functionality, allowing wider application of this technology outside biomedical engineering laboratories.

We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used Cauchy mutation along a random direction in the parametric space. Secondly, relatively large number of elite organisms (6-10% of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quality GA performance. GA was validated against optical mapping recordings of human ventricular AP and mRNA expression profile of donor hearts. In particular, GA output parameters were rescaled proportionally to mRNA levels ratio between patients. We have demonstrated that mRNA-based models predict the AP waveform dependence on heart rate with high precision. The latter also provides a novel technique of model personalization that makes it possible to map gene expression profile to cardiac function.