科研之友
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摘要
Purpose: Models based on a sum of damped exponentials occur in many applications, particularly in multicomponent T-2 relaxometry. The problem of estimating the relaxation parameters and the corresponding amplitudes is known to be difficult, especially as the number of components increases. In this article, the commonly used non-negative least squares spectrum approach is compared to a recently published estimation algorithm abbreviated as Exponential Analysis via System Identification using Steiglitz-McBride. Methods: The two algorithms are evaluated via simulation, and their performance is compared to a statistical benchmark on precision given by the Cramer-Rao bound. By applying the algorithms to an in vivo brain multi-echo spin-echo dataset, containing 32 images, estimates of the myelin water fraction are computed. Results: Exponential Analysis via System Identification using Steiglitz-McBride is shown to have superior performance when applied to simulated T-2 relaxation data. For the in vivo brain, Exponential Analysis via System Identification using Steiglitz-McBride gives an myelin water fraction map with a more concentrated distribution of myelin water and less noise, compared to non-negative least squares. Conclusion: The Exponential Analysis via System Identification using Steiglitz-McBride algorithm provides an efficient and user-parameter-free alternative to non-negative least squares for estimating the parameters of multiple relaxation components and gives a new way of estimating the spatial variations of myelin in the brain.
- 出版日期2016-1
