An image-based modeling framework for patient-specific computational hemodynamics

Abstract

We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine.

Patient-Specific 3D Simulation of Cyclic CSF Flow at the Craniocervical Region

“…6 During this process the surface was smoothed with a passband of 0.1. The investigator performed some editing to facilitate the subsequent simulations, including adjustment of the resolution of the model to achieve the desired anatomic detail 7 required and to eliminate undesired structures such as blood vessels in the subarachnoid space.…”

Simulated hemodynamics in human carotid bifurcation based on Doppler ultrasound data

“…CFD, applied to realistic, three-dimensional arterial geometries derived from clinical imaging, provides an accurate assessment of flow patterns and shear stress in complex geometries [7,8].…”

Cardiovascular morphometry with high-resolution 3D magnetic resonance: First application to left ventricle diastolic dysfunction

“…In more detail, the centerline C is defined and calculated as the locus of the centers of the maximal inscribed spheres along the cardiovascular region of interest. The centerlines are estimated automatically in a form of discrete 3D point sets using the Vascular Modeling Toolkit software (VMTK, www.vmtk.org ) [32] . The calculation of local and global features for morphometry characterization is affected by the noise in the estimation of the 3D centerline curves.…”