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Abstract #3328

Helmholtz Inversion using Unconstrained Optimization for MR Elastography of the Lung: A Comparison to Direct Inversion

Huiming Dong1,2, Rizwan Ahmad2, and Arunark Kolipaka1,2
1Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, United States, 2Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States

Lung stiffness is a potential biomarker for multiple lung diseases. MR elastography (MRE) allows non-invasive measurement of lung stiffness. However, it is challenging to estimate stiffness using direct Helmholtz inversion due to low signal-to-noise ratio (SNR) from lung MRE. In this work, a compressed-sensing-based Helmholtz inversion is proposed where noise is reduced via Laplacian of Gaussian (LoG) and Morozov’s discrepancy principle, while the sparsity of stiffness map is explored in a wavelet domain. Results demonstrated that the proposed inversion yielded robust stiffness estimation and successfully detected higher stiffness at total lung capacity (TLC) compared to residual volume (RV).

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