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Abstract #4000

Curating dataset for AI -based Stiffness Estimation in MR Elastography Using Finite Element Modeling and Polynomial Curve Fitting

Hassan Iftikhar1, Rizwan Ahmad1, and Arunark Kolipaka2
1Biomedical Engineering, The Ohio State University, Columbus, OH, United States, 2Radiology, The Ohio State University, Columbus, OH, United States

Synopsis

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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