Calculating tissue shear modulus and pressure by 2D log-elastographic methods

Joyce R. McLaughlin, Ning Zhang, Armando Manduca

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Shear modulus imaging, often called elastography, enables detection and characterization of tissue abnormalities. In this paper the data are two displacement components obtained from successive MR or ultrasound data sets acquired while the tissue is excited mechanically. A 2D plane strain elastic model is assumed to govern the 2D displacement, u. The shear modulus, μ, is unknown and whether or not the first Lamé parameter, λ, is known the pressure p = λ∇ u which is present in the plane strain model cannot be measured and is unreliably computed from measured data and can be shown to be an order one quantity in the units kPa. So here we present a 2D log-elastographic inverse algorithm that (1) simultaneously reconstructs the shear modulus, μ, and p, which together satisfy a first-order partial differential equation system, with the goal of imaging μ; (2) controls potential exponential growth in the numerical error and (3) reliably reconstructs the quantity p in the inverse algorithm as compared to the same quantity computed with a forward algorithm. This work generalizes the log-elastographic algorithm in Lin et al (2009 Inverse Problems 25) which uses one displacement component, is derived assuming that the component satisfies the wave equation and is tested on synthetic data computed with the wave equation model. The 2D log-elastographic algorithm is tested on 2D synthetic data and 2D in vivo data from Mayo Clinic. We also exhibit examples to show that the 2D log-elastographic algorithm improves the quality of the recovered images as compared to the log-elastographic and direct inversion algorithms.

Original languageEnglish (US)
Article number085007
JournalInverse Problems
Volume26
Issue number8
DOIs
StatePublished - 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Mathematical Physics
  • Computer Science Applications
  • Applied Mathematics

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