Modeling, simulation and experimental data

Cardiovascular: Segmentation and blood flow simulations of patient-specific heart data

Dimitri Metaxas, Scott Kulp, Mingchen Gao, Shaoting Zhang, Zhen Qian, Leon Axel

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, we present a fully automatic and accurate segmentation framework for 2D cardiac tagged MR images, a semiautomatic method for 3D segmentation from CT data, and the results of blood flow simulation using these highly detailed models. The 2D segmentation system consists of a semiautomatic segmentation framework to obtain the training contours, and a learning-based framework that is trained by the semiautomatic results, and achieves fully automatic and accurate segmentation. We then present a method to simulate and visualize blood flow through the human heart, using the reconstructed 4D motion of the endocardial surface of the left ventricle as boundary conditions. The reconstruction captures the motion of the full 3D surfaces of the complex features, such as the papillary muscles and the ventricular trabeculae. We use visualizations of the flow field to view the interactions between the blood and the trabeculae in far more detail than has been achieved previously, which promises to give a better understanding of cardiac flow. Finally, we use our simulation results to compare the blood flow within one healthy heart and two diseased hearts.

Original languageEnglish (US)
Title of host publicationComputational Surgery and Dual Training
Subtitle of host publicationComputing, Robotics and Imaging
PublisherSpringer New York
Pages213-240
Number of pages28
ISBN (Electronic)9781461486480
ISBN (Print)9781461486473
DOIs
StatePublished - Jan 1 2014

Fingerprint

Flow simulation
Blood
Computer simulation
Muscle
Flow fields
Visualization
Boundary conditions

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Adaboost learning
  • Deformable model
  • Ejection fraction
  • Flow visualization
  • Heart disease
  • Heart flow
  • Hemodynamic
  • Image segmentation
  • Magnetic resonance images
  • Metamorphs segmentation
  • Patient specific simulation
  • Shape model
  • Valves deformation

Cite this

Metaxas, D., Kulp, S., Gao, M., Zhang, S., Qian, Z., & Axel, L. (2014). Modeling, simulation and experimental data: Cardiovascular: Segmentation and blood flow simulations of patient-specific heart data. In Computational Surgery and Dual Training: Computing, Robotics and Imaging (pp. 213-240). Springer New York. https://doi.org/10.1007/978-1-4614-8648-0_14
Metaxas, Dimitri ; Kulp, Scott ; Gao, Mingchen ; Zhang, Shaoting ; Qian, Zhen ; Axel, Leon. / Modeling, simulation and experimental data : Cardiovascular: Segmentation and blood flow simulations of patient-specific heart data. Computational Surgery and Dual Training: Computing, Robotics and Imaging. Springer New York, 2014. pp. 213-240
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Metaxas, D, Kulp, S, Gao, M, Zhang, S, Qian, Z & Axel, L 2014, Modeling, simulation and experimental data: Cardiovascular: Segmentation and blood flow simulations of patient-specific heart data. in Computational Surgery and Dual Training: Computing, Robotics and Imaging. Springer New York, pp. 213-240. https://doi.org/10.1007/978-1-4614-8648-0_14

Modeling, simulation and experimental data : Cardiovascular: Segmentation and blood flow simulations of patient-specific heart data. / Metaxas, Dimitri; Kulp, Scott; Gao, Mingchen; Zhang, Shaoting; Qian, Zhen; Axel, Leon.

Computational Surgery and Dual Training: Computing, Robotics and Imaging. Springer New York, 2014. p. 213-240.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Metaxas D, Kulp S, Gao M, Zhang S, Qian Z, Axel L. Modeling, simulation and experimental data: Cardiovascular: Segmentation and blood flow simulations of patient-specific heart data. In Computational Surgery and Dual Training: Computing, Robotics and Imaging. Springer New York. 2014. p. 213-240 https://doi.org/10.1007/978-1-4614-8648-0_14