Abstract
Patient-specific biomechanical simulations of the behavior of soft tissue play an important role in surgery assistance systems. They can provide surgeons with valuable insights and additional information for diagnosis and therapy. In this work, we focus on the surgical treatment of incompetent mitral valves by means of minimally-invasive mitral valve reconstructions (MVR). We aim at supporting MVR surgery by providing adequate FEM-based soft tissue simulations, which simulate the behavior of the patient-individual mitral valve subject to natural forces during the cardiac cycle and to surgical manipulation during MVR. In order to forster the applicability of such simulations, they need to be fully integrated in often complex biomechanical modeling workflows, which cover the complete pipeline from imaging and segmentation to meshing, model setup and simulation. Like this, an automated execution of all preprocessing steps and of the simulation itself can be enabled. However, patient-specifity and workflow automation often do not fit together, and are sometimes conflicting, such that the respective biomechanical models and the results of corresponding simulation scenarios quickly turn unrealistic or even misleading for surgery assistance. Therefore, we have set up a comprehensive pipeline of geometry analytics tools and simulation setup operators, which allow for defining and facilitating patient-specific MVR surgery simulation scenarios. Based on these scenarios and on the therein referred pipeline-produced simulation input data, we can feed and run our dedicated MVR simulation application which is built on top of the open-source C++ FEM software toolkit HiFlow3. In our work, we thus explain the concept and implementation of the pre- and post-processing operators of such MVR simulations, and point out specific features and problems arising therein. Also, we describe the integration of our operator pipeline into the framework of the Medical Simulation Markup Language (MSML), which shall allow for an automated and more general usage. Finally, we present a set of exemplary results of an application of our operator pipeline to real MVR patient data and explain how the respective model and simulation represent real anatomy and the surgical intervention of an MVR. Concluding, we discuss the suggested setup and give an outlook on possible future developments.
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