The strain in the left ventricle is a well-known biomarker for cardiac diseases. Nowadays, several acquisition techniques have been developed to improve the diagnose of this kind of conditions. Usually, strain biomarkers are obtained by mean of image post-processing techniques using different deformation metrics. In this work we present a numerical framework for the generation of left-ventricle strain phantoms using three different acquisition sequences in order to provide a broad database of patients and volunteers with different types of diseases. Our library provides a robust image generation tool to compare and develop new post-processing methods for quantifying strain phantoms
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