2012 Proceedings - International Tissue Elasticity Conference

055 MULTI–SCALE COMPRESSION–BASED QUANTITATIVE ELASTOGRAPHY AND ITS
APPLICATION TO BLOOD PRESSURE ESTIMATION.
AM Zakrzewski 1 , SY Sun 1 , MW Gilbertson 1 , B Vannah 1 , L Chai 1 , J Ramos 1 , BW Anthony 1 .
1 Massachusetts Institute of Technology, Cambridge, MA, USA.
Background: Quantitative compression–based elastography provides a method for doctors to identify
pathological tumors using elastic modulus estimations. Current quantitative implementations of
elastography use few assumptions but must be faster for use in a clinical setting.
Aims: This study aims to improve the speed of current elastography methods and to apply these methods
to estimate blood pressure and arterial wall stiffness.
Methods: In order to generate the necessary input into the quantitative elastography model, pre–compression
and post–compression radio frequency data are simulated with Field II while a cross–correlation displacement
tracking technique estimates the tissue displacement field [1]. The inverse problem is uniquely solved in
Matlab using the Levenberg–Marquardt non–linear optimization scheme [2], and the associated geometrically
non–linear, materially linear elastic forward problem is solved in the commercially available finite element
package Abaqus. A coarse–to–fine multi–scale approach is used such that each finite element’s elastic
modulus is iteratively interpolated onto an increasingly finer mesh. In order to confirm the validity and utility
of the multi–scale algorithm, elastography was performed on experimental copolymer and mineral oil
phantoms [3] using a Terason 3000t system and a high–fidelity force–controlled ultrasound probe [4]. The
multi–scale technique is also used to estimate simultaneously the elasticity of soft tissue, blood pressure, and
arterial wall stiffness from radio frequency data simulated with Field II. In order to solve this inverse problem
and minimize the objective function, the blood pressure and arterial wall stiffness are included as additional
variables to optimize, along with the material stiffness of each finite element.
Results: Using the multi–scale approach described above, the elastic modulus of soft tissue containing a
stiff inclusion is reconstructed in 20–25 iterations to within 5% of the true value in simulations and to
within 5% of the measured value in phantoms. Simulation models that include blood pressure and an
arterial wall (Figure 1) reconstruct the pressure, artery stiffness and bulk stiffness to within 5% of the
known values. The accuracy of the blood pressure and artery model suggests that this method can be
applied clinically for non–invasive continuous blood pressure measurement.
(a) (b) (c)
Figure 1: Elastography performed on simulated data to reconstruct arterial wall stiffness, blood pressure and soft
tissue stiffness. (a) The B–Mode image is shown with an artery and blood pressure; (b) the axial
displacement has been calculated; (c) the elasticity distribution is shown. In this Figure, the true soft
tissue stiffness, arterial stiffness and blood pressure are 15kPa, 400kPa and 80mmHg, respectively.
Acknowledgements: This work was supported by General Electric Global Research based in Niskayuna, NY.
Kai Thomenius is acknowledged for his insight and suggestions to improve this work.
References:
[1] Sun SY, Anthony BW, Gilbertson MW: Trajectory–Based Deformation Correction in Ultrasound Images. SPIE
Conf. Medical Imaging, 2010.
[2] Han L, Noble JA, and Burcher M: The Elastic Reconstruction of Soft Tissue. IEEE Int. Symp. Biomedical Imaging,
pp. 1035–1038, 2002.
[3] Oudry J, Bastard C, Miette V, Willinger R, Sandrin L: Copolymer–in–Oil Phantom Materials for Elastography.
Ultrasound Med. Biol., Vol. 35, pp.1185–1197, 2009.
[4] Gilbertson MW, Anthony BW: Impedance–Controlled Ultrasound Probe. SPIE Conf. Medical Imaging, 2011.
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