Patient-Specific Model Generation and Simulation for Pre-operative Surgical Guidance for Pulmonary Embolism Treatment

  • Shankar P. Sastry
  • Jibum Kim
  • Suzanne M. Shontz
  • Brent A. Craven
  • Frank C. Lynch
  • Keefe B. Manning
  • Thap Panitanarak
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 3)


Pulmonary embolism (PE) is a potentially-fatal disease in which blood clots (i.e., emboli) break free from the deep veins in the body and migrate to the lungs. In order to prevent PE, anticoagulation therapy is often used; however, for some patients, it is contraindicated. For such patients, a mechanical filter, namely an inferior vena cava (IVC) filter, is inserted into the IVC to capture and prevent emboli from reaching the lungs. There are numerous IVC filter designs, and it is not well understood which particular IVC filter geometry will result in the best clinical outcome for a given patient. Patient-specific computational fluid dynamic (CFD) simulations may be used to aid physicians in IVC filter selection and placement. In particular, such computational simulations may be used to determine the capability of various IVC filters in various positions to capture emboli, while not creating additional emboli or significantly altering the flow of blood in the IVC. In this paper, we propose a computational pipeline that can be used to generate patient-specific geometric models and computational meshes of the IVC and IVC filter for various IVC anatomies based on the patient’s computer tomography (CT) images. Our pipeline involves several steps including image processing, geometric model construction, surface and volume mesh generation, and CFD simulation. We then use our patient-specific meshes of the IVC and IVC filter in CFD simulations of blood flow, whereby we demonstrate the potential utility of this approach for optimized, patient-specific IVC filter selection and placement for improved prevention of PE. The novelty of our approach lies in the use of a superelastic mesh warping technique to virtually place the surface mesh of the IVC filter (which was created via computer-aided design modeling) inside the surface mesh of the patient-specific IVC, reconstructed from clinical CT data. We also employ a linear elastic mesh warping technique to simulate the deformation of the IVC when the IVC filter is placed inside of it.


Inferior Vena Cava Computational Fluid Dynamic Simulation Surface Mesh Inferior Vena Cava Filter Computer Tomography Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Rick Schraf and Todd Fetterolf for creating the CAD model of the IVC filter. They would also like to thank Katerina Papoulia, of the University of Waterloo, for helpful discussions on the superelasticity computations. The work of the first three authors was supported in part by NSF CAREER Award OCI-1054459. The work of the first, third, fifth, and sixth authors was supported in part by an Institute for Cyberscience grant from The Pennsylvania State University. The work of the third and fifth authors was also supported in part by a grant from the Grace Woodward Foundation at The Pennsylvania State University. The work of the third author was also supported in part by NSF grant CNS-0720749. The work of the seventh author was supported by a Royal Thai Government Scholarship.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shankar P. Sastry
    • 1
  • Jibum Kim
    • 1
  • Suzanne M. Shontz
    • 1
  • Brent A. Craven
    • 2
  • Frank C. Lynch
    • 3
  • Keefe B. Manning
    • 1
  • Thap Panitanarak
    • 1
  1. 1.The Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Penn State Applied Research LaboratoryUniversity ParkUSA
  3. 3.Penn State Milton S. Hershey Medical CenterHersheyUSA

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