Most traditional methods for T(1) map estimation in MRI with fast low-angle-shot sequences are aimed at high efficiency by compromising the fitting accuracy. In this paper, the fundamental problem of parameter estimation in fast low-angle-shot MRI was re-examined, and an accurate and fast optimization approach, named concatenated optimization for parameter estimation, was proposed for the regression of data points acquired with multiple flip angles. The initial estimation of T(1) was obtained from the linear regression, followed by the constrained nonlinear regression based on the initial estimates. This heterogeneous initialization strategy improves the fitting accuracy and reduces the computational time. A computationally efficient implementation of concatenated optimization for parameter estimation was achieved based on the graphic processing unit, named as concatenated optimization for parameter estimation graphic processing unit. In experimental comparison with Fram's method and the Fitter Tool in Jim, the proposed methods are capable of achieving significantly higher efficiency and more accurate estimations.
(c) 2010 Wiley-Liss, Inc.