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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