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Probabilistic Elastography: Estimating Lung Elasticity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6801)

Abstract

We formulate registration-based elastography in a probabilistic framework and apply it to study lung elasticity in the presence of emphysematous and fibrotic tissue. The elasticity calculations are based on a Finite Element discretization of a linear elastic biomechanical model. We marginalize over the boundary conditions (deformation) of the biomechanical model to determine the posterior distribution over elasticity parameters. Image similarity is included in the likelihood, an elastic prior is included to constrain the boundary conditions, while a Markov model is used to spatially smooth the inhomogeneous elasticity. We use a Markov Chain Monte Carlo (MCMC) technique to characterize the posterior distribution over elasticity from which we extract the most probable elasticity as well as the uncertainty of this estimate. Even though registration-based lung elastography with inhomogeneous elasticity is challenging due the problem’s highly underdetermined nature and the sparse image information available in lung CT, we show promising preliminary results on estimating lung elasticity contrast in the presence of emphysematous and fibrotic tissue.

Keywords

Posterior Distribution Markov Chain Monte Carlo Elastic Parameter Biomechanical Model Markov Chain Monte Carlo Method 
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.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Surgical Planning Lab, Department of RadiologyHarvard Medical School, Brigham and Women’s HospitalBoston
  2. 2.Pulmonary and Critical Division, Department of MedicineHarvard Medical School, Brigham and Women’s HospitalBoston

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