In this study, we propose a generalized linear solution to estimate dynamic relaxation parameters R2* and R2 from combined spin- and gradient-echo (SAGE) MRI. We compared linear least squares (LLSQ) to nonlinear least squares (NLSQ) using Monte-Carlo simulated data and in vivo whole-brain data with varying signal-to-noise ratios (SNR). We show that using the LLSQ is both computationally more efficient and retains NLSQ precision, producing nearly the same estimates of R2* and R2 in a fraction of the time. This approach is widely extendable to other multi-echo, multi-contrast MRI methods, with applications including both perfusion MRI and functional MRI.
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