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Abstract #3210

A Model-based Neural Network for shear modulus estimation of MRE

Runke Wang1, Yu Chen1, Suhao Qiu1, and Yuan Feng1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China


Algorithms providing accurate estimation of shear modulus and identification of tissue boundary are always desired for magnetic resonance elastography (MRE). In this study, we proposed a model-based neural network (MNN) embedding classic direct inversion (DI) and local frequency estimation (LFE). Convolution layers with Inception-like structure were applied for preprocessing, while postprocessing was implemented with a U-net structure. Additionally, DI and LFE algorithms were encapsulated as layers transforming wave images to shear modulus maps. The network performance was tested using a phantom with an inclusion. Compared with conventional algorithms, MNN provided modulus estimation with 5-fold higher contrast-to-noise ratio with clear boundaries.

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