Optical Ultrastructure of Cardiac Tissue Helps to Reproduce Discordant Alternans by In Silico Data Assimilation

A relevant issue in cardiology is represented by identifying valuable biomarkers of cardiac dysfunctions and by designing reliable computational models to predict transitions into pathological cardiac dynamics. In this context, alternans regimes have been proven to anticipate tachycardia and fibrillation. Still, an open problem is defining accurate and convenient methods to predict the onset and evolution of alternans patterns and formulate reliable models reproducing alternans features as observed in experiments. In this contribution, we present an FFT-based method on voltage mapping data, named FFI (Fast-Fourier-Imaging), which is able to early identify alternating cardiac dynamics and recover tissue structural information. Our results show that FFI identifies alternans patterns with great accuracy, avoiding excessive data preprocessing required by other methods. The extracted optical ultrastructural details of the tissue are used to inform computational parameters by accurate data assimilation, which enables the in-silico recovery of the experimental ex-vivo observations of a canine heart.

The application of FFI analysis enables the almost real-time detection of concordant and discordant alternans patterns in cardiac tissue and opens the way to new mathematical approaches with significant impacts on personalized modeling and whole organ simulations.