Department of Mechanical Science and Engineering
University of Illinois at Urbana-Champaign
Doctors use palpation on a daily basis to aid in diagnosis of diseases that change tissue properties. Magnetic Resonance Elastography (MRE) is a promising non-invasive substitute for manual palpation. It has already been proven clinically for staging the stiffening of liver tissue from liver fibrosis. The present studies look to extend the technique to the human brain. The human brain is an ideal organ for MRE because the skull makes it difficult to access, and because of the vast number of diseases affecting the microstructure of the brain. The accuracy of MRE is affected by the direction of wave propagation relative to the orientation of axon bundles that make up the microstructure, potentially affecting the validity of the isotropic material model. In an effort to increase the fidelity of MRE, our study has analyzed the result of introducing more than one excitation direction in the Non-Linear Inversion (NLI) methodology [1-2]. External excitation is applied at the back of the head in an anterior-posterior direction and at the side of the head in a left-right direction. The steady-state displacement fields are imaged using specialized MR imaging, and material properties are estimated using NLI. The combination of multiple excitation fields within NLI results in higher-fidelity properties, which we hope will increase sensitivity to changes in the brain’s microstructure.
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Aaron Anderson is a Theoretical and Applied Mechanics Ph.D. candidate in the Mechanical Science and Engineering department at the University of Illinois at Urbana-Champaign. He holds a B.A. in physics from the University of Wisconsin-Eau Claire, a B.S. in mechanical engineering from the University of Wisconsin-Madison, and an M.S. in TAM from the University of Illinois. Under the guidance of Prof. John Georgiadis, his thesis research is focused on increasing the specificity of Magnetic Resonance Elastography to aid in the diagnosis and staging of neurodegenerative diseases through improved material models.