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SimVascular is an open source software suite for cardiovascular simulation, providing a complete pipeline from medical image data to 3D model construction, meshing, and blood flow simulation. SimVacular represents the state of the art in cardiovascular simulation, including advanced tools for physiologic boundary conditions, fluid structure interaction, and an accurate and efficient finite element Navier-Stokes solver. SimVascular integrates custom code with best-in-class open source packages to support clinical and basic science research. Three-dimensional modeling and simulation of cardiovascular hemodynamics can provide invaluable data to augment clinical imaging and predict outcomes of surgical interventions for individual patients. However, cardiovascular patient-specific modeling requires a complex multi-step process of image segmentation, meshing, flow simulation, and analysis which is often daunting for the user. To meet these needs, SimVascular ( was initially released in 2007 by Stanford University. It is currently the only open source software package that provides a complete pipeline from medical image based modeling to patient specific blood flow simulation and analysis and has contributed to numerous advances in fundamental hemodynamics research, surgical planning, and medical device design. SimVascular Pipeline: In the typical SimVascular workflow, vessel paths (centerlines) are created first based on a patient medical image. Along each path, a group of two-dimensional contours of the vessel lumen is defined via specialized segmentation method including level set, threshold, analytic fitting, and manual segmentation with various editing tools. Batch segmentation by level set is implemented for automated segmentation of an entire vessel with minimal user intervention. By lofting and unioning the groups of 2D segmentations, a complete 3D model of the vasculature of interest is obtained. From the solid model a tetrahedral unstructured mesh with enhanced quality is generated using several local refinement tools. Mesh adaption tools utilizing a-posteriori error estimates are also integrated. To perform hemodynamic simulations at physiologically realistic flow rates and pressures, time dependent flow rates or heart models are defined for the inlet(s), and boundary condition parameters are specified at the outlets by fitting a lumped parameter model to match a set of clinical data. Simulations can be run with a single core or with multiple cores at a computer desktop or cluster. Finally, relevant hemodynamic quantities such as velocity, pressure, wall shear stress, oscillatory index are calculated from the simulation results. Recent Advances of SimVascular: Early versions of SimVascular had several major barriers preventing wider adoption by new users and limited its application in large-scale clinical and research studies. With recent funding from the NSF, the SimVascular development team has made significant progress reducing the barriers to adoption by integrating open source alternatives for the expensive commercial libraries previously required for anatomic modeling, mesh generation, mesh adaptation, and the linear solver. In addition, improved methods for more efficient level-set based 2D and 3D image segmentation have been included. New features include robust Boolean operations for discrete triangulated surfaces, algorithms to combine 2D and 3D image segmentations in a single model, and constrained local surface smoothing operations. The flow solver capabilities have been extended to include back-flow stabilization, multi-scale coupling for closed loop boundary conditions, coronary boundary conditions, and fluid-structure interaction (FSI) for deformable vessels with variable wall properties. A new modular framework is being developed with a modern graphical user interface incorporating Qt, Python, and functionality from the Medical Imaging Interaction Toolkit (MITK). The new interface providing increased DICOM support and improvements to the user experience during modeling and simulation. Summary: SimVascular has been significantly improved throughout the pipeline with expanded functionality and increased ease of use. Since the inception of the revitalization project in 2013, SimVascular has attracted nearly 1200 unique users worldwide and has been utilized in more than 80 abstracts and journal publications. Active research and development continues on new image segmentation techniques based on machine learning and neural networks as well as on algorithms to convert discrete surface triangulations directly to analytic surface models. Work on an optimization module to enable users to automate device design and perform virtual surgical planning is also underway.





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  • (2024), "SimVascular,"

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