Patient-specific modeling of stress/strain for surgical planning and guidance.

C. Sprouse, D. DeMenthon, J. Gammie, Philippe Burlina

Research output: Contribution to journalArticle

Abstract

We describe a method for performing modeling and simulation to predict the strain and stress experienced by tissues resulting from reconstructive cardiothoracic surgery. Stress computation is an important predictor of the quality and longevity of a repair and can therefore be used as guidance by a surgeon when deciding between various repair options. This paper uses the mitral valve repair as a use case because of its relevance and prevalence among reconstructive cardiac interventions. The modeling method presented here is informed by the patient specific anatomical structure recovered from real time 3D echocardiography. The method exploits hyperelastic models to infer realistic strain-stresses. We show through experiments using actual clinical data that results are in line with physiological expectations.

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Repair
Planning
Reconstructive Surgical Procedures
Three-Dimensional Echocardiography
Echocardiography
Mitral Valve
Surgery
Tissue
Patient-Specific Modeling
Experiments
Surgeons

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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abstract = "We describe a method for performing modeling and simulation to predict the strain and stress experienced by tissues resulting from reconstructive cardiothoracic surgery. Stress computation is an important predictor of the quality and longevity of a repair and can therefore be used as guidance by a surgeon when deciding between various repair options. This paper uses the mitral valve repair as a use case because of its relevance and prevalence among reconstructive cardiac interventions. The modeling method presented here is informed by the patient specific anatomical structure recovered from real time 3D echocardiography. The method exploits hyperelastic models to infer realistic strain-stresses. We show through experiments using actual clinical data that results are in line with physiological expectations.",
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