Structural Edge Detection for Cardiovascular Modeling

Merkow, Jameson; Tu, Zhuowen; Kriegman, David; Marsden, Alison L.

doi:10.1007/978-3-319-24574-4_88

Cited by 12 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, new methods for image segmentation based on machine learning and artificial neural networks are in development. 38 These methods will provide users with improved and automated image-to-model capabilities to reduce the time to construct accurate image-based models. In addition, modules for optimization, uncertainty quantification, and parameter estimation to match clinical data are under development.…”

Section: Discussionmentioning

confidence: 99%

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

et al. 2016

Self Cite

View full text Add to dashboard Cite

Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic, translational and clinical research. In this paper we discuss the recent development of a fully open-source SimVascular software package, which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis. This package serves as a research tool for cardiovascular modeling and simulation, and has contributed to numerous advances in personalized medicine, surgical planning and medical device design. The SimVascular software has recently been refactored and expanded to enhance functionality, usability, efficiency and accuracy of image-based patient-specific modeling tools. Moreover, SimVascular previously required several licensed components that hindered new user adoption and code management and our recent developments have replaced these commercial components to create a fully open source pipeline. These developments foster advances in cardiovascular modeling research, increased collaboration, standardization of methods, and a growing developer community.

show abstract

Section: Discussionmentioning

confidence: 99%

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…SimVascular is an actively maintained open source project, with additional enhancements and new features in preparation. For image segmentation, deep learning-based 2D segmentation has been explored with the goal to speed up the image-based anatomic modeling process and make it possible for large scale application of cardiovascular simulation on clinical studies [78,79]. These tools will be added to the GUI in future releases.…”

Section: Discussionmentioning

confidence: 99%

A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package

Lan

Updegrove

Wilson

et al. 2018

Journal of Biomechanical Engineering

View full text Add to dashboard Cite

Patient-specific simulation plays an important role in cardiovascular disease research, diagnosis, surgical planning and medical device design, as well as education in cardiovascular biomechanics. simvascular is an open-source software package encompassing an entire cardiovascular modeling and simulation pipeline from image segmentation, three-dimensional (3D) solid modeling, and mesh generation, to patient-specific simulation and analysis. SimVascular is widely used for cardiovascular basic science and clinical research as well as education, following increased adoption by users and development of a GATEWAY web portal to facilitate educational access. Initial efforts of the project focused on replacing commercial packages with open-source alternatives and adding increased functionality for multiscale modeling, fluid-structure interaction (FSI), and solid modeling operations. In this paper, we introduce a major SimVascular (SV) release that includes a new graphical user interface (GUI) designed to improve user experience. Additional improvements include enhanced data/project management, interactive tools to facilitate user interaction, new boundary condition (BC) functionality, plug-in mechanism to increase modularity, a new 3D segmentation tool, and new computer-aided design (CAD)-based solid modeling capabilities. Here, we focus on major changes to the software platform and outline features added in this new release. We also briefly describe our recent experiences using SimVascular in the classroom for bioengineering education.

show abstract

“…This was in large part due to the significant time currently required for model construction and simulation. Current efforts are geared towards accelerating the model building and simulation processes through integration of machine learning and automation algorithms [43]. …”

Section: Discussionmentioning

confidence: 99%

Patient-Specific Simulations Reveal Significant Differences in Mechanical Stimuli in Venous and Arterial Coronary Grafts

Ramachandra

Kahn

Marsden

2016

J. of Cardiovasc. Trans. Res.

View full text Add to dashboard Cite

Mechanical stimuli are key to understanding disease progression and clinically observed differences in failure rates between arterial and venous grafts following coronary artery bypass graft surgery. We quantify biologically relevant mechanical stimuli, not available from standard imaging, in patient-specific simulations incorporating non-invasive clinical data. We couple CFD with closed-loop circulatory physiology models to quantify biologically relevant indices, including wall shear, oscillatory shear, and wall strain. We account for vessel-specific material properties in simulating vessel wall deformation. Wall shear was significantly lower (p = 0.014*) and atheroprone area significantly higher (p = 0.040*) in venous compared to arterial grafts. Wall strain in venous grafts was significantly lower (p = 0.003*) than in arterial grafts while no significant difference was observed in oscillatory shear index. Simulations demonstrate significant differences in mechanical stimuli acting on venous vs. arterial grafts, in line with clinically observed graft failure rates, offering a promising avenue for stratifying patients at risk for graft failure.

show abstract

Structural Edge Detection for Cardiovascular Modeling

Cited by 12 publications

References 8 publications

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package

Patient-Specific Simulations Reveal Significant Differences in Mechanical Stimuli in Venous and Arterial Coronary Grafts

Contact Info

Product

Resources

About