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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Committee:
Ajit P. Yoganathan, PhD (Advisor)
Jarek Rossignac, PhD
Robert Guldberg, PhD
Mitral valve (MV) disease is responsible for approximately 2,581 deaths and 41,000 hospital discharges each year in the US. Mitral regurgitation (MR), retrograde blood from through the MV, is often an indicator of MV disease. Surgical repair of MVs is preferred over replacement, as it is correlated with better patient quality of life. However, replacement rates are still near 40% because MV surgical repair expertise is not spread across all hospitals. In addition, 15-80% of surgical repair patients have recurrent MR within 10 years. Quantitative patient-specific models could aid these issues by providing less experienced surgeons with additional information before surgery and a quantitative map of patient valve changes after surgery.
Real-time 3D echocardiography (RT3DE) can provide high quality 3D images of MVs and has been used to generate quantitative models previously. However, there is not currently an efficient, dynamic, and validated method that is fast enough to use in common practice. To fill this need, a tool to generate quantitative 3D models of mitral valve leaflets from RT3DE in an efficient manner was created. Then an in vitro echocardiography correction scheme was devised and a dynamic, in vitro validation of the tool was performed. The tool demonstrated that it could generate dynamic, complex MV geometry accurately and more efficiently than current methods available. In addition, the ability for mesh interpolation techniques to reduce segmentation time was demonstrated.
The tool generated by this study provides a method to quickly and accurately generate MV geometry that could be applied to dynamic patient specific geometry to aid surgical decisions and track patient geometry changes after surgery.
