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Abstract #3537

Impact of Curl Operation on Stiffness Estimation in Brain MR Elastography

KowsalyaDevi Pavuluri1, Arvin Arani1, Joshua D. Trzasko1, Matthew L. Senjem1,2, John Huston III1, Richard L. Ehman1, Armando Manduca1,3, and Matthew C. Murphy1
1Department of Radiology, Mayo Clinic, Rochester, Rochester, MN, United States, 2Department of Information Technology, Mayo Clinic, Rochester, Rochester, MN, United States, 3Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Rochester, MN, United States

Synopsis

Keywords: Elastography, Elastography, Stiffness, Curl, MR Elastography (MRE), Neural Network Inversions

Motivation: To improve the reliability of MRE in stiffness estimation.

Goal(s): To assess how curl and gradient-based methods compare in estimating stiffness and preserving biological contrasts in MRE.

Approach: Direct inversion (DI) and neural network inversion (NNI) methods were evaluated on simulated and in vivo data using both curl and gradient displacement field as input features.

Results: Curl operator-based stiffness estimates were softer, consistent with more complete removal of the longitudinal wave. However, both curl-based and gradient-based methods captured biologically relevant contrasts.

Impact: The findings provide insights for selecting MRE processing methods, indicating that while curl-based methods improve shear wave isolation, gradient-based approaches may serve as better training data for capturing stiffness variations in structurally complex, inhomogeneous tissues.

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