Keywords: Other AI/ML, Elastography, AI, Deep Learning
Motivation: The estimation of stiffness in Magnetic Resonance Elastography (MRE) yields erroneous estimates, due to inability of current algorithms to correctly estimate propagating wavelengths.
Goal(s): How to curate the dataset, if AI is to be employed as an algorithm to estimate the stiffness values in MRE.
Approach: We designed a pipeline to curate a supervised learning dataset using Finite Element Modelling, and Polynomial Curve Fitting.
Results: The dataset curated by our pipeline was then used to estimate the stiffness values using supervised learning, with U-Net as our model's architecture; the model's performance was evaluated on some human liver geometries.
Impact: Stiffness of the soft tissues is an important biomarker for detecting various pathological states. This method enhances the MRE by enabling the precise tissue stiffness estimation, advancing non-invasive diagnostics of diseases like fibrosis, and cancer.
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