Controlled Augmentations of Left Ventricle Deformation for Optical Flow Model Training

Accurate analysis of 3D myocardium deformation using high-resolution computed 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 augmentation-based approach for enriching cardiovascular imaging datasets and improving machine learning (ML) model performance. We introduce a controlled deformation augmentation method, enriched with crucial GT 3D optical flow annotations, designed to support the training and validation of ML and artificial intelligence (AI) algorithms for cardiac motion estimation. We outline the data preparation from a cardiac four-dimensional (4D) CT scan, the selection of parameters, and the subsequent creation of augmented deformation samples 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 AI-based myocardium deformation analysis algorithms for clinical applications and diagnostics.