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Abstract #1139

Inversion Parameters based on Convergence and Error Metrics for Nonlinear Inversion MR Elastography

Aaron T Anderson1,2, Curtis L Johnson3, Matthew DJ McGarry4,5, Keith D Paulsen4,6, Bradley P Sutton2,7, Elijah EW Van Houten4,8, and John G Georgiadis9

1Mechanical Science & Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Biomedical Engineering, University of Delaware, Newark, DE, United States, 4Thayer School of Engineering, Dartmouth College, Hanover, NH, United States, 5Biomedical Engineering, Columbia University, New York, NY, United States, 6Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States, 7Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 8Génie Mécanique, Université de Sherbrooke, Sherbrooke, QC, Canada, 9Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States

Nonlinear inversion (NLI) of brain MRE data has shown the promise in sensitive detection of complex neurodegenerative disease by showing repeatable and accurate assessments of both white matter and gray matter regions in healthy subjects. This study looks to further improve the accuracy of the NLI-MRE framework by characterizing two major inversion parameters: subzone size and conjugate gradient iterations. Additionally, two convergence criteria are proposed as means to quantify the confidence in final reported statistics while fully capturing the distribution of heterogeneity within white matter regions.

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