  Accurate analysis of 3D myocardium deformation using high-resolution
computerized tomography (CT) datasets with ground truth (GT) annotations is
crucial for advancing cardiovascular imaging research. However, the scarcity of
such datasets poses a significant challenge for developing robust myocardium
deformation analysis models. To address this, we propose a novel approach to
synthetic data generation for enriching cardiovascular imaging datasets.
  We introduce a synthetic data generation method, enriched with crucial GT 3D
optical flow annotations. We outline the data preparation from a cardiac
four-dimensional (4D) CT scan, selection of parameters, and the subsequent
creation of synthetic data from the same or other sources of 3D cardiac CT data
for training.
  Our work contributes to overcoming the limitations imposed by the scarcity of
high-resolution CT datasets with precise annotations, thereby facilitating the
development of accurate and reliable myocardium deformation analysis algorithms
for clinical applications and diagnostics.
  Our code is available at:
http://www.github.com/shaharzuler/cardio_volume_skewer
