  In silico tissue models enable evaluating quantitative models of magnetic
resonance imaging. This includes validating and sensitivity analysis of imaging
biomarkers and tissue microstructure parameters. We propose a novel method to
generate a realistic numerical phantom of myocardial microstructure. We extend
previous studies accounting for the cardiomyocyte shape variability, water
exchange between the cardiomyocytes (intercalated discs), myocardial
microstructure disarray, and four sheetlet orientations. In the first stage of
the method, cardiomyocytes and sheetlets are generated by considering the shape
variability and intercalated discs in cardiomyocyte-to-cardiomyocyte
connections. Sheetlets are then aggregated and oriented in the directions of
interest. Our morphometric study demonstrates no significant difference
($p>0.01$) between the distribution of volume, length, and primary and
secondary axes of the numerical and real (literature) cardiomyocyte data.
Structural correlation analysis validates that the in-silico tissue is in the
same class of disorderliness as the real tissue. Additionally, the absolute
angle differences between the simulated helical angle (HA) and input HA
(reference value) of the cardiomyocytes ($4.3^\circ\pm 3.1^\circ$) demonstrate
a good agreement with the absolute angle difference between the measured HA
using experimental cardiac diffusion tensor imaging (cDTI) and histology
(reference value) reported by (Holmes et al., 2000) ($3.7^\circ\pm6.4^\circ$)
and (Scollan et al., 1998) ($4.9^\circ\pm 14.6^\circ$). The angular distance
between eigenvectors and sheetlet angles of the input and simulated cDTI is
smaller than those between measured angles using structural tensor imaging
(gold standard) and experimental cDTI. These results confirm that the proposed
method can generate richer numerical phantoms for the myocardium than previous
studies.
