Early detection of cardiac disease is essential to optimize treatment and patient follow-up, but also to reduce its associated mortality and morbidity. Various cardiac imaging modalities are already available for the cardiologist, mainly providing information on tissue morphology and structure. However, none of these imaging methods is able to directly measure stresses or intrinsic mechanical properties of the heart, which are potential key diagnostic markers to distinguish between normal and abnormal physiology. Therefore, we explore the potential of shear wave imaging, an ultrasound-based technique, to non-invasively measure myocardial stiffness. The technique studies a natural or acoustically generated vibration in the tissue of interest to determine its propagation speed, which is linked to tissue stiffness. This allows shear wave imaging to identify regions with higher stiffness, which is associated with pathology. SWE has shown to be successful in detecting tumors in breast tissue and fibrosis in liver tissue; however application of SWE to the heart is more challenging due to the complex mechanical and structural properties of the heart. Therefore, an increased shear wave speed might be related to other factors than pathology and this master thesis investigates the cofounders that complicate interpretation of SWE measurements.
This thesis frames within a larger project where experimental data has been collected from 13 pigs during different conditions (pre/afterload alteration, dobutamine administration, myocardial infarct induction). The goal of this thesis is to post-process the collected experimental data (shear wave measurements and pressure-volume data) and perform statistical analyses to investigate the sensitivity of the SWE technique in the cardiac setting. This project is in collaboration with the Cardiovascular Imaging and Dynamics lab (KULeuven, Leuven) and Thorax Center for Biomedical Engineering (Erasmus MC, Rotterdam).
The project will mainly require the use of Matlab.