Background: Clinical multi-electrode mapping of atrial fibrillation (AF) drivers suffers from variable contact, signal processing, and structural complexity within the 3D human atrial wall, raising questions on the validity of such drivers. Objectives: To improve AF driver identification by integrating clinical multi-electrode mapping with driver fingerprints defined by high-resolution ex-vivo 3D functional and structural imaging. Methods: Sustained AF was mapped in coronary-perfused explanted human hearts (n=11) with transmural near-infrared optical mapping (NIOM, ~0.3mm2 resolution). Simultaneously, custom FIRMap catheters (~9×9mm2 resolution) mapped endocardial and epicardial surfaces, which were analyzed by Focal Impulse and Rotor Mapping (FIRM) activation and Rotational Activity Profile (RAP). Functional maps were integrated with contrast-enhanced MRI (CE-MRI, ~0.1mm3 resolution) analysis of 3D fibrosis architecture. Results: During sustained AF, NIOM identified 1-2 intramural, spatially stable reentrant AF drivers per heart. Driver targeted ablation affecting 2.2±1.1% of the atrial surface terminated and prevented AF. Driver regions had significantly higher phase singularity density, and dominant frequency versus neighboring non-driver regions. FIRM had 80% sensitivity to NIOM-defined driver locations (16/20), and matched 14/20 driver visualizations: 10/14 reentries seen with RAP, and 4/6 breakthrough/focal patterns. FIRM detected 1.1±0.9 false-positive RAP per recording, but these regions had lower intramural CE-MRI fibrosis than driver regions (14.9±7.9% vs 23.2±10.5%, p<0.005). Conclusion: The study revealed that both reentrant and breakthrough/focal AF driver patterns visualized by surface-only clinical multi-electrodes can represent projections of 3D intramural microanatomic reentries. Integration of multi-electrode mapping and 3D fibrosis analysis may enhance AF driver detection, thereby improving the efficacy of driver targeted ablation.

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.

Background - Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multi-electrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of Machine Learning (ML) to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings. Methods - Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM) (0.3mm 2 resolution) and 64-electrode MEM (Higher-Density (HD) or Lower-Density (LD) with 3mm 2 and 9mm 2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier Transform analysis into 28407 total Fourier spectra. Thirty-five features for ML were extracted from each Fourier spectrum. Results - Targeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated classifications for driver vs non-driver electrodes in MEM arrays. Compared to analysis of single electrogram frequency features, averaging the features for each surrounding 8 electrodes neighborhood, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation including driver periphery electrodes were added to driver center annotation. Notably, f1-score for the binary classification of HD catheter dataset were significantly higher than that of LD catheter (0.81 ± 0.02 vs 0.66 ± 0.04, p<0.05). The trained algorithm correctly highlighted 86% of driver regions with HD but only 80% with LD MEM arrays (81% for LD+HD arrays together). Conclusions - The ML model pre-trained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or non-driver compared to the NIOM gold-standard. Future application of NIOM-validated ML approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Background Atrial fibrillation (AF) driver mechanisms are obscured to clinical multielectrode mapping approaches that provide partial, surface‐only visualization of unstable 3‐dimensional atrial conduction. We hypothesized that transient modulation of refractoriness by pharmacologic challenge during multielectrode mapping improves visualization of hidden paths of reentrant AF drivers for targeted ablation. Methods and Results Pharmacologic challenge with adenosine was tested in ex vivo human hearts with a history of AF and cardiac diseases by multielectrode and high‐resolution subsurface near‐infrared optical mapping, integrated with 3‐dimensional structural imaging and heart‐specific computational simulations. Adenosine challenge was also studied on acutely terminated AF drivers in 10 patients with persistent AF. Ex vivo, adenosine stabilized reentrant driver paths within arrhythmogenic fibrotic hubs and improved visualization of reentrant paths, previously seen as focal or unstable breakthrough activation pattern, for targeted AF ablation. Computational simulations suggested that shortening of atrial refractoriness by adenosine may (1) improve driver stability by annihilating spatially unstable functional blocks and tightening reentrant circuits around fibrotic substrates, thus unmasking the common reentrant path; and (2) destabilize already stable reentrant drivers along fibrotic substrates by accelerating competing fibrillatory wavelets or secondary drivers. In patients with persistent AF, adenosine challenge unmasked hidden common reentry paths (9/15 AF drivers, 41±26% to 68±25% visualization), but worsened visualization of previously visible reentry paths (6/15, 74±14% to 34±12%). AF driver ablation led to acute termination of AF. Conclusions Our ex vivo to in vivo human translational study suggests that transiently altering atrial refractoriness can stabilize reentrant paths and unmask arrhythmogenic hubs to guide targeted AF driver ablation treatment.