Michael Sacks, a professor of Biomedical Engineering at UT Austin, and his team outlined their computational modelling technique for imaging MV leaflets - flaps located on the base of the valve that open and close to regulate blood flow from the left atrium to left ventricle of the heart - in recent issues of the International Journal for Numerical Methods in Biomedical Engineering and the Annals of Biomedical Engineering .
The MV plays a crucial role in maintaining healthy blood flow in the heart, but normal function can be compromised in a number of ways. For instance, heart attacks may disrupt the MV leaflets' capacity to close properly, resulting in blood leaking back into the heart's left atrium. The importance of healthy MV function is thus widely understood within the medical community, but there is not a consensus on how best to treat common MV disorders such as regurgitation, prolapse and mitral valve stenosis.
Until now, there has been a lack of accurate modelling approaches available to surgeons for predicting the best surgical methods to restore MV function.
"Heart valves are very difficult to study. They are complex structures that move incredibly fast and are located inside the heart, making them extremely difficult to image", stated Michael Sacks, who also serves as director of the James T. Willerson Center for Cardiovascular Modeling and Simulation in the university's Oden Institute for Computational Engineering and Sciences. "Our new computational model provides surgeons with a tool for the prediction of post-surgical outcomes from clinically obtained pre-surgical data alone."
Michael Sacks has spent most of his academic career analyzing and modelling heart valve function. Recent advances in computational and 3D imaging technologies have made it possible for Michael Sacks and his team to non-invasively and accurately acquire the in vivo - or living - geometry of the MV leaflets in patients from real-time 3D echocardiography - a clinical technique that uses sound waves to monitor heart function.
The UT team's computational model was developed in collaboration with researchers from Penn Medicine and Georgia Tech.
"Because we run high-fidelity patient-specific models, the computational demands are substantial, and the use of TACC allows us to explore various modeling approaches in a much more efficient manner", Michael Sacks stated. "We can now explore in-silico in days what used to take weeks or even months to simulate."
Michael Sacks has been an active user of advanced computing resources from the Texas Advanced Computing Center (TACC) at UT Austin since 2011. For this research, Michael Sacks and his research team used TACC's Lonestar5 and Stampede2 supercomputers.
"Our models combined the complete 3D geometry of the mitral valve in the open and closed states, making possible an unparalleled level of predictive accuracy", Michael Sacks stated. "To model the MV leaflets, we then integrated into the MV models the structure and mechanical properties of the internal constituents, such as the collagen fibers which make up most of the valve, to develop attribute-rich complete MV models."
Several studies have shown significant deficiencies in the long-term success of current surgical approaches to treating common heart valve diseases. Up to 60 percent of patients who have undergone MV regurgitation surgery report recurrence just two years after the surgery.
"Cardiac surgeons must decide upon the best possible treatment for heart valve repair without knowing all the facts", stated Robert Gorman, professor of surgery in the Perelman School of Medicine at the University of Pennsylvania and a key collaborator on the study. "Most rely on their own experiences or how they were taught to perform valve repair surgery in medical school."
With the researchers' new computational predictive technique, surgeons won't have to take the previous one-size-fits-all approach to MV leaflet repair.
Michael Sacks stated: "The current computational model is a step forward because it utilizes and is derived from patient-specific imaging data. We can now accurately predict what the post-surgical state of the microvalve will be based on pre-surgical data."
"The computational modeling tool we've developed will eliminate a lot of the uncertainty and allow for patient specificity", Robert Gorman stated. "This will be transformative for those working in the field."
The next steps for Michael Sacks and his research team is to conduct multi-center trials as part of commercializing their technique.
"Once heart surgeons gain access to this tool in a clinical setting, we anticipate significant improvements in the long-term well-being of patients who've undergone mitral valve surgery", he stated.
The study was funded by the National Institutes of Health.