2012 Proceedings - International Tissue Elasticity Conference

072 INFERRING TISSUE MICROSTUCTURE USING ELASICITY IMAGING.
Assad A. Oberai 1 , Elizabete Rodrigues–Ferreira 1,2 , Paul E. Barbone 3 , Timothy J. Hall 4
1 Rensselaer Polytechnic Institute, 110 8 th Street, Troy, NY, 12180, USA; 2 Université Libre de
Bruxelles, Campus Plaine C.P. 218/1, 1050 Bruxelles, BELGIUM; 3 Boston University,
110 Cummington Street, Boston, MA 12180, USA; 4 University of Wisconsin–Madison, 1005 WIMR,
1111 Highland Avenue, Madison, WI 53706, USA.
Background: The progression of cancer is accompanied by changes in the micro–structural organization
of tissue [1]. In particular, it has been observed that an increasingly invasive phenotype is accompanied
by an increase in the concentration of collagen fiber bundles and their reorganization from a highly coiled
state to a straight, almost rod–like state. These changes in the microstructural organization lead to
changes in macroscopic properties. For example, a higher concentration of collagen implies a larger linear
elastic modulus and a decrease in tortuosity would mean a smaller “toe” region in the stress–strain curve.
Aims: The main aim of our study is to demonstrate that elasticity imaging techniques that are used to
determine linear and nonlinear elastic properties can be extended in order to create images of average
micro–structural properties of tissue. Further, that these images may be useful in diagnosing malignant
tumors.
Methods: This aim is achieved through 1) Developing a nonlinear, elastic constitutive model derived from
tissue micro–structure that contains fiber bundle concentration and tortuosity as parameters. 2)
Implementing this model in an inverse problem framework in order to determine the spatial distribution
of these parameters from measured displacements. 3) Using displacement measured in benign and
malignant tumors in order to test the hypothesis that the spatial distribution of micro–structural
parameters can be used to diagnose cancerous tumors.
Results: The response from the nonlinear constitutive law derived from the tissue–microstructure was
analyzed in a uniaxial stress–stretch test. It was found that the fiber bundle concentration determined
the initial elastic modulus and fiber bundle tortuosity determined the strain at which the departure from
a linear stress–strain behavior takes place. Tissue displacement data from a small set of patients was
used in order to create images of fiber bundle concentration and tortuosity. These images were analyzed
in order to establish correlation between these properties and malignancy. It was found that fiber bundle
tortuosity held the potential of being able to diagnose cancerous tumors.
Conclusions: An elasticity imaging based approach for determining the average micro–structural
properties of tissue was developed and implemented. Its potential in diagnosing malignant tumors in a
small set of patients was also examined.
Acknowledgements: Support from the NIH and the NSF is acknowledged.
References:
[1] P. Schedin, and P.J. Keely: Mammary Gland ECM Remodeling, Stiffness, and Mechanosignaling in Normal
Development and Tumor Progression. Cold Spring Harbor perspectives in Biology, 3(1), a003228. doi:10.1101/, 2011.
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