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Dynamic Elastography

Electrical Impedance Tomography

Geophysical Fault Identification

Geotechnical Identification

Radar Imaging

Quasi Static Elastography

Ultrasound cancer image on left, shear wave front speed cancer image using arrival time algorithm on right. (In Vivo Data M. Fink, M. Tanter, ESPCI.)
Dynamic Elastography
Precise and quick imaging of human tissue is a critical need.

High-quality images targeted to differentiate between benign and cancerous inclusions can offer doctors an improved ability to locate abnormal tissue.

IPRPI researchers use elastography – the process of imaging elastic tissue properties – to create images that display stiffness changes in the body, in order to improve the detection of such abnormalities.

Much can be learned from examining elastic properties of tissues, since they are directly related to the underlying structure of the tissue and are strongly affected by pathological changes.

Tumor inclusions or tissue blocked from its blood nutrients are stiffer than normal tissue; benign and cancerous tumors also have distinguishing elastic properties.

How this project works
This project targets low frequency shear wave propagation, since the shear wave moves forward significantly faster in abnormal tissue.

For abnormalities, the speed can be two to four times that of normal tissue. The speed is then a direct measure of tissue stiffness.

The propagating wave is measured with an ultrasound system (selected for its low cost) or an MR system (to image deeper in the tissue).

Team members use data from a wave and sound laboratory at the Ecole Superieure de Physique et de Chimie Industrielles (ESPCI) in Paris. This lab is led by ESPCI Professor Mathias Fink, who has developed a high-speed data acquisition system that is able to image a wave propagating in tissue at 5,000 frames per second.

Team members use data from the laboratory of Kevin Parker, with radiologist Deborah Rubens, at the University of Rochester Medical School. The laboratory produces a slowly propegating traveling wave from two sources simultaneously oscillating. The Rensselaer, University of Rochester project is joint with G.E.

Advantages of this hybrid physics or coupled physics method
The advantage of the combined experiment of a low frequency propagating shear wave measured with a high frequency ultrasound acquisition system or an MR system is that wave speed changes in abnormal tissue may be detected in images based on sequences of ultrasound B-scan data or on sequences of MR databases.

A further expected advantage is that shear wave propagation in benign tissue will be distinguished from different propagation in cancerous tissue. The resulting image will provide an important diagnostic tool to the medical community.

An important new application is in liver disease. Doctors at the Mayo Clinic have found that the liver becomes stiff before fibrosis and cirrhosi set in: this finding provides a significant early detection tool for liver disease.

Four key challenges
Rensselaer's elastography project has four key challenges:

  • Finding the best quality data sets
  • Creating mathematical models that distinguish abnormalities
  • Locating stable image constructions that are not sensitive to noise
  • Providing rapid results

Significant Advances
This project has made significant advances:

  • An arrival time algorithm, which uses level set methods and is based on the positions of the propagating front of the shear wave, has been successfully tested for sensitivities with synthetic data, has been applied to in vivo and in vitro data from breast and prostate.
  • Finite element models determine sensitivity of images due to noise.
  • Multiple data sets are utilized to improve algorithmic stability.
  • Log-elastographic and implicit algorithms provide stable reconstructions from single frequency oxcillation data.
  • Theoretical results establish the richness of the time and space dependent wave propagation data; conditional stability results show single data set reconstructions can be stable.
  • All of the reconstruction methods address the ill-posedness of the problem: the constructed solution is sensitive to noise, unless "intelligent" algorithms, such as the one mentioned above, are developed.

Rapid progress for this elastography project has been possible due to funding provided by:

  • National Science Foundation
  • National Institutes of Health
  • Office of Naval Research

Additional graduate student support has come from a GAANN award.

Project members
Under the leadership of Dr. Joyce McLaughlin, Rensselaer professor of mathematical sciences and IPRPI director, this interdisciplinary team includes professors, postdoctoral fellows, and graduate students in Rensselaer's departments of Mathematical Sciences and Mechanical, Aerospace, and Nuclear Engineering.

Rensselaer Faculty
Joyce R. McLaughlin
Antoinette Maniatty
Assad Oberai

Research Scientists
Dan Renzi
Jeong-Rock Yoon
Ashley Thomas
Jessica Jones

Completed Graduate Students
Dan Renzi, Thesis:"Shear Wave Speed Recovery in Transient Elastography"
Kui Lin Thesis:"Error Estimation for the Direct Inversion Model and Numberical Schemes for the Full Inverstion Model in Elastography"
Ning Zhang, Thesis: "2D Log-Elastographic Methods for Tissue Stiffness Reconstruction Using a 2D Plane Elastic System"
Ashley Thomas, Thesis: "Shear Wave Speed Recovery in Crawling Wave Sonoelastography"
Jessica Jones, Thesis: "Statistical Comparrison of Shear Wave Speed Recovery Using the Direct Algorithm and the Arrival Time Algorithm and the Arrival Time Alogrithm"

Undergraduate Students
Michael Pathawala


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