Abstract #1139

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

Aaron T Anderson^1,2, Curtis L Johnson³, Matthew DJ McGarry^4,5, Keith D Paulsen^4,6, Bradley P Sutton^2,7, Elijah EW Van Houten^4,8, 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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