Cardiovascular disease is the number one cause of death globally; early detection is crucial for patient outcome. To complement traditional clinical investigations, and to gain new insights into the function and physiology of heart disease, a simulation framework based on clinical CT data was developed and applied to a cohort of 12 patients with suspected cardiac disease. Results were compared to clinical flow measurements using MRI.
To compute intracardiac blood flow patterns in the left atrium and ventricle, the CFX flow solver was coupled to ICEM to automatically remesh the model in order to handle the extremely complex motion during the cardiac cycle. Computed flow fields together with clinical flow measurements from MRI were visualized in Ensight and showed excellent agreement. The simulation framework is also capable of computing flow in the coronary arteries, the right side of the heart, and in mechanical heart valves. Consequently, the modeling approach can complement traditional clinical imaging techniques with information that cannot be measured. Simulations can also optimize the prosthetic heart valve’s position, size and orientation before going into surgery. Clinical decisions will become more detailed and specific when augmented by modeling and simulation approaches, which will benefit both the individual patient in terms of a more specific diagnosis, and society in general, with lower healthcare costs.