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Abstract #3236

Parameter estimation with matrix-based signal models using VARPRO for transient- and steady-state imaging

Nam Gyun Lee1 and Krishna S. Nayak2
1Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, 2Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States

VARPRO-based parameter estimation is extensively used in MR for its improved accuracy, precision, and convergence behavior compared to general nonlinear least-squares algorithms. This study investigates the feasibility of using matrix-based signal models with VARPRO instead of conventional analytic signal equations. Simulations and in-vivo study show that VARPRO with matrix-based signal models is identical to VARPRO with analytic signal equations, and both VARPRO approaches provide enhanced precision and accuracy in relaxometry maps compared to the conventional DESPOT1/2 methods from variable flip angle SPGR and bSSFP measurements.

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