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

By Luca Antiga, Marina Piccinelli, Lorenzo Botti, Bogdan Ene-Iordache, Andrea Remuzzi, David A. Steinman. Medical and Biological Engineering and Computing, 2008

875 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/da59de0fc7d3000d26d2c473e5ab7a5d72d3d87f

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In Computational Mechanics

  1. Marino Arroyo
  2. Daniel Millán
  3. Alejandro Torres-S'anchez

In Computer Graphics and Computer-Aided Design

  1. Daniel Millán

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