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Abstract #4456

Robust nonlinear parameter estimation with physics-enforced spectral-spatial priors for multi-pool CEST analysis

Chinh Dinh Nguyen1, Roh-Eul Yoo2, Seung Hong Choi2, and Jaeseok Park1,3
1Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, Republic of, 2Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of, 3Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of

Synopsis

Keywords: Quantitative Imaging, Tumors, head, multi-pool, Lorentzian, separable nonlinear

Motivation: Asymmetric spectral analysis of multi-pool CEST MRI directly from largely incomplete measurements is challenging due to its nonlinearity and data scarcity in $$$k$$$-$$$w$$$ space. Conventional Lorentzian analysis is limited, considering only one-dimensional variation in spectral signals while not fully exploiting physics constraints.

Goal(s): To develop a robust parameter estimation method for multi-pool CEST spectral analysis with highly incomplete measurements.

Approach: We proposed a multi-pool CEST-specific, nonlinear separable model with physics-constrained spectral-spatial priors and performed patient studies with brain tumors.

Results: The proposed method yields clear CEST parameter maps in brain tumor regions, demonstrating feasible whole-brain CEST MRI (1.8mm$$$^3$$$) in under 4 minutes.

Impact: The proposed method yields robust multi-pool CEST spectral analysis even with highly incomplete measurements, enabling whole brain CEST MRI (1.8mm$$$^3$$$) in less than 4 minutes.

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