Probabilistic elastography: estimating lung elasticity

Authors:
Petter Risholm;James Ross;George R. Washko;William M. Wells
Affiliations:
Surgical Planning Lab, Department of Radiology;Pulmonary and Critical Division, Department of Medicine Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts;Pulmonary and Critical Division, Department of Medicine Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts;Surgical Planning Lab, Department of Radiology
Venue:
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Year:
2011

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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.