Neural Network-based MR Elastography Wave Inversion Using Physics-based Simulations and Uncertainty Quantification

Motivation: In Magnetic Resonance Elastography (MRE), accurate reconstruction of stiffness maps is essential for medical diagnosis. Traditional inversion techniques are limited by noise, discretization and/or low wavenumbers. Goal(s): We aim to overcome these limitations using a neural network-based wave inversion (ElastoNet) with integrated uncertainty quantification ensuring reliable predictions with high detail resolution. Approach: We trained ElastoNet on simulated wave patches. For inference, we combined all 3 motion encoding directions as input and used evidential deep learning as an uncertainty quantification method. Results: ElastoNet achieves a substantial improvement in detail resolution compared to current neural network approaches and shows promising results in the low-frequency domain. Impact: Our MR elastography neural network-based wave inversion is a promising method for enhanced accuracy and reliability in tissue property characterization. It effectively addresses challenges in reconstruction of stiffness maps, expanding the potential of MR elastography for medical diagnosis.