  Monitoring the cardiac function often relies on non-invasive vital
measurements, electrocardiograms, and low-resolution echocardiograms. The
monitoring data can be augmented with numerical modeling results to support
treatment-risk assessment. Often, medical images are not suitable for
high-fidelity modeling due to their spatial sparsity and low resolution. In
this work, we present a workflow that converts a limited number of
two-dimensional (2D) echocardiogram images into analysis-suitable
left-ventricle geometries using isogeometric analysis (IGA). The image data is
fitted by reshaping a multi-patch NURBS template. This fitting procedure
results in a smooth interpolation with a limited number of degrees of freedom.
In addition, a coupled 3D-0D cardiac model is extended with a pre-stressing
method to perform ventricular-cardiac-mechanic analyses based on in vivo
images. The workflow is benchmarked using population-averaged imaging data,
which demonstrates that both the shape of the left ventricle and its mechanical
response are well predicted in sparse-data scenarios. The case of a clinical
patient is considered to demonstrate the suitability of the workflow in a
real-data setting.
