Joint Localization and Parameter Estimation for Localized Calcium Release Events in Video Microscopy

This paper addresses modeling and inference for localized calcium release events observed in cardiac muscle tissue known as sparks, a recently discovered and little-understood phenomenon thought to be central to cardiac pathophysiology and hence of much scientific interest. In contrast to earlier algorithms proposed for studying sparks observed via confocal line-scan data, we consider here a model for the spatiotemporal spark process in unpaced cardiac myocytes observed in vitro via fluorescence video microscopy. In particular, we focus on parametric modeling of the spark process, leading naturally to a framework for joint localization and estimation of spark events and their parameters in space and time, along with the ability to quantify the uncertainty of our resulting inference. We show that for simulated sparks derived from real micropatterned cardiac myocyte data, and for which ground truth is known, our method significantly outperforms a video-adapted version of the most popular spark localization method currently in use in the cardiac biophysics community. Importantly, the framework we propose extends naturally to other other applications of video microscopy, and also allows for subsequent refinement of both the models and fitting procedures described here.