  Accurate assessment of myocardial tissue stiffness is pivotal for the
diagnosis and prognosis of heart diseases. Left ventricular diastolic stiffness
($\beta$) obtained from the end-diastolic pressure-volume relationship (EDPVR)
has conventionally been utilized as a representative metric of myocardial
stiffness. The EDPVR can be employed to estimate the intrinsic stiffness of
myocardial tissues through image-based in-silico inverse optimization. However,
whether $\beta$, as an organ-level metric, accurately represents the
tissue-level myocardial tissue stiffness in healthy and diseased myocardium
remains elusive. We developed a modeling-based approach utilizing a
two-parameter material model for the myocardium (denoted by $a_f$ and $b_f$) in
image-based in-silico biventricular heart models to generate EDPVRs for
different material parameters. Our results indicated a variable relationship
between $\beta$ and the material parameters depending on the range of the
parameters. Interestingly, $\beta$ showed a very low sensitivity to $a_f$, once
averaged across several LV geometries, and even a negative correlation with
$a_f$ for small values of $a_f$. These findings call for a critical assessment
of the reliability and confoundedness of EDPVR-derived metrics to represent
tissue-level myocardial stiffness. Our results also underscore the necessity to
explore image-based in-silico frameworks, promising to provide a high-fidelity
and potentially non-invasive assessment of myocardial stiffness.
