SlicerHeart: An open-source computing platform for cardiac image analysis and modeling.

Cardiovascular disease is a significant cause of morbidity and mortality in the developed world. 3D imaging of the heart's structure is critical to the understanding and treatment of cardiovascular disease. However, open-source tools for image analysis of cardiac images, particularly 3D echocardiographic (3DE) data, are limited. We describe the rationale, development, implementation, and application of SlicerHeart, a cardiac-focused toolkit for image analysis built upon 3D Slicer, an open-source image computing platform. We designed and implemented multiple Python scripted modules within 3D Slicer to import, register, and view 3DE data, including new code to volume render and crop 3DE. In addition, we developed dedicated workflows for the modeling and quantitative analysis of multi-modality image-derived heart models, including heart valves. Finally, we created and integrated new functionality to facilitate the planning of cardiac interventions and surgery. We demonstrate application of SlicerHeart to a diverse range of cardiovascular modeling and simulation including volume rendering of 3DE images, mitral valve modeling, transcatheter device modeling, and planning of complex surgical intervention such as cardiac baffle creation. SlicerHeart is an evolving open-source image processing platform based on 3D Slicer initiated to support the investigation and treatment of congenital heart disease. The technology in SlicerHeart provides a robust foundation for 3D image-based investigation in cardiovascular medicine.

Modeling Tool for Rapid Virtual Planning of the Intracardiac Baffle in Double-Outlet Right Ventricle.

PURPOSE: Biventricular repair of double-outlet right ventricle (DORV) necessitates the creation of a complex intracardiac baffle. Creation of the optimal baffle design and placement thereof can be challenging to conceptualize, even with 2-dimensional and 3-dimensional images. This report describes a recently developed methodology for creating virtual baffles to inform intraoperative repair. DESCRIPTION: A total of 3 heart models of DORV were created from cardiac magnetic resonance images. Baffles were created and visualized using custom software. EVALUATION: This report demonstrates application of the tool to virtual planning of the baffle for repair of DORV in 3 cases. Models were examined by a multidisciplinary team, on screen and in virtual reality. Baffles could be rapidly created and revised to facilitate planning of the surgical procedure. CONCLUSIONS: Virtual modeling of the baffle pathway by using cardiac magnetic resonance, creation of physical templates for the baffle, and visualization in virtual reality are feasible and may be beneficial for preoperative planning of complex biventricular repairs in DORV. Further work is needed to demonstrate clinical benefit or improvement in outcomes.