Heart valve disease is estimated to cost over a billion dollars in the Unites States annually and mitral valve disorders account for the largest portion of the disease with treatments involving valve repair and replacement (Iung et. al). It is estimated that an experienced surgeon can effectively repair 95 - 100 percent of mitral valve (MV) disorders but a recent study showed that the average rate of MV repair is 41 percent. The current paradigm in treatments include diagnostic imaging data to see the current state of the patient, empirical data from previous similar cases to evaluate the efficacy of prior treatment and the judgment of the surgeon. Due to immense variability amongst patients in terms of anatomy and physiological conditions, this data alone is insufficient to predict outcomes of treatments. We therefore propose a new paradigm of combining imaging techniques and computational modeling allowing for patient specific modeling of anatomical structures and finite element analysis to predict surgical outcomes. Performing this approach currently requires extensive knowledge of the particular fields and can be difficult to follow for a non-expert. We are thus in the process of developing a user friendly tool which combines these techniques into a single framework which can easily be used by non-experts. The hypothesis of this work is that providing surgeons with prior knowledge of post-repair dynamics of a diseased MV will improve surgical decision making and increase repair rates. This work outlines a framework of user interactive tools for simulating the common techniques used by surgeons to correct mitral regurgitation. This toolset is built as an extension of IA-FEMesh (Grosland et. al) and enables MV leaflet mesh and chordae tendinae mesh generation from point cloud data. It also includes tools for repairing the chordae tendinae, resecting a portion of the valve, applying MV material properties, boundary conditions and exporting these as an Abaqus (.inp) file where the simulation is carried out. The results of these simulations ultimately serve as biomechanical markers for identifying the best surgical method to be used.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-5358 |
| Date | 01 July 2014 |
| Creators | Gade, Piyusha Sanjay |
| Contributors | Vigmostad, Sarah Celeste |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright 2014 Piyusha Sanjay Gade |
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