Computational model of the heart’s mitral valve presurgery. The new imaging technology is so accurate, it can be used in a clinical setting to provide patient-specific models, so surgeons can tailor treatments for mitral valve diseases on a case by case basis. [Image from Cockrell School of Engineering, The University of Texas at Austin]
The computational modeling technique the researchers used images of mitral valve leaflets. The mitral valve is important for maintaining healthy blood flow in the heart, but a heart attack could hinder its capabilities to close properly, causing blood to leak back into the heart.
While there are still studies being done on mitral valve repair devices, there is a lack of accurate modeling approaches for surgeons to predict the best surgical methods that can restore mitral valve function, according to the researchers.
“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,” Michael Sacks, a professor in UT Austin’s department of biomedical engineering and a researcher on the project, said in a press release. “Our new computational model provides surgeons with a tool for the prediction of post-surgical outcomes from clinically obtained presurgical data alone.”
Sacks and his research team gathered the living geometry of the mitral valve leaflets using a real-time 3D echocardiography, which is a clinical technique that uses sound waves to monitor heart function. The computational method was developed in a collaboration with researchers from Penn Medicine and Georgia Tech.
“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,” Sacks said. “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.”
According to the researchers, about 60% of patients who have had mitral valve regurgitation surgery have reported recurrence two years after surgery. The researchers also suggest that there are a number of studies that show significant deficiencies in the success of current surgical approaches long-term.
“Cardiac surgeons must decide upon the best possible treatment for heart valve repair without knowing all the facts,” Dr. Robert Gorman, a professor of surgery 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.”
Using the new predictive technique, the researchers no longer have to use the “one-size-fits-all” approach.
“The computational modeling tool we’ve developed will eliminate a lot of the uncertainty and allow for patient specificity,” Gorman said. “This will be transformative for those working in the field.”
The researchers now plan to commercialize 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,” Gorman said.
The research was published in the International Journal for Numerical Methods in Biomedical Engineering and the Annals of Biomedical Engineering. The research was funded by the National Institutes of Health.
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