  The personalization of cardiac models is the cornerstone of patient-specific
modeling. Ideally, non-invasive or minimally-invasive clinical data, such as
the standard ECG or intracardiac contact recordings, could provide an insight
on model parameters. Parameter selection of such models is however a
challenging and potentially time-consuming task.
  In this work, we estimate the earliest activation sites governing the cardiac
electrical activation. Specifically, we introduce GEASI (Geodesic-based
Earliest Activation Sites Identification) as a novel approach to simultaneously
identify their locations and times. To this end, we start from the anisotropic
eikonal equation modeling cardiac electrical activation and exploit its
Hamilton-Jacobi formulation to minimize a given objective functional, which in
the case of GEASI is the quadratic mismatch to given activation measurements.
This versatile approach can be extended for computing topological gradients to
estimate the number of sites, or fitting a given ECG.
  We conducted various experiments in 2D and 3D for in-silico models and an
in-vivo intracardiac recording collected from a patient undergoing cardiac
resynchronization therapy. The results demonstrate the clinical applicability
of GEASI for potential future personalized models and clinical intervention.
