Abstract
Myocardial infarction, a leading cause of mortality worldwide, leaves survivors at significant risk of recurrence caused by scar-related re-entrant ventricular tachyarrhythmias. Effective treatment with ablation therapy requires a precise guidance system. Non-linear optical microscopy techniques, such as second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), are promising candidates for a high-resolution alternative to conventional electrical mapping for assessing infarcted cardiac tissue. Here, we apply SHG and TPEF with a resolution advantage over commonly used electrical mapping techniques to assess ex-vivo sheep heart infarction. Analyzing conventional and radiomic features allows for quantitative characterization of scar tissue. Our machine learning classifier achieved high accuracy, offering a promising, data-driven approach for guiding in-situ ablation therapy with increased precision. This study represents a significant step towards integrating quantitative image analysis in therapeutic interventions.