Title: Biomechanical Imaging in Tissue
Organizer(s): Joyce R. McLaughlin, Rensselaer Polytechnic Institute
Gen Nakamura, Hokkaido University
Speakers: Armando Manduca and Richard Ehman, Mayo Clinic
Magnetic Resonance Elastography: Challenges and Opportunities

Mark Palmeri, Duke University Medical School
Quantitative Shear Wave Elasticity Imaging Techniques to Noninvasively Characterize Soft Tissue Stiffness

Joyce McLaughlin, Rensselaer Polytechnic Institute
Biomechanical Imaging in Tissue-Using Time dependent Data and Frequency Dependent Data

Gen Nakamura and Yu Jiang, Hokkaido University
Data Analysis for Micro-MRE measured data

Abstract: Biomechanical Imaging, with dynamically acquired data, is inspired by the doctor\u2019s palpation exam where the doctor presses against the skin to feel stiff abnormal regions. The first goal then, in biomechanical imaging with dynamic data, is to dynamically apply a force that moves the tissue in such a way that a shear wave propagates in the tissue. A shear wave is targeted because it is a very slowly propagating wave in tissue which is 80-90% fluid. The second goal is to measure the displacement of the wave throughout the tissue. This is accomplished by taking advantage of technologies that are based on different physical principles. In one case the fact that the compression or pressure wave propagates at nearly 1500 m/sec enables taking sequential RF/IQ ultrasound data acquisitions to produce a movie of the propagating shear waves. In the second case, the concept of MRI imaging is utilized so that sequential MRI data sets can be processed to create a data set of a harmonic oscillating shear wave. Unlike the ultrasound data acquisition, MRI imaging is a slow data acquisition modality so that several snap shots of the propagating shear waves are more preferable as the data set. In both cases the data set of the propagating wave are obtained. Finally, the inverse problem is addressed. That is, once the data set is obtained, the mathematical model for the data must be formulated, algorithms for recovering the biomechanical parameters utilizing the mathematical model are developed, and images of biomechanical parameters are created. The talks in this minisymposium will address all these issues, with a focus on the inverse problem aspects.

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