Vanhamme L, Van Huffel S, Van Hecke P, van Ormondt D
Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kard. Mercierlaan 94, Leuven, 3001, Belgium.
J Magn Reson. 1999 Sep;140(1):120-30. doi: 10.1006/jmre.1999.1835.
Quantification of individual magnetic resonance spectroscopy (MRS) signals is possible in the time domain using interactive nonlinear least-squares fitting methods which provide maximum likelihood parameter estimates under certain assumptions or using fully automatic, but statistically suboptimal, black-box methods. In kinetic experiments time series of consecutive MRS spectra are measured in which information concerning the time evolution of some of the signal parameters is often present. The purpose of this paper is to show how AMARES, a representative example of the interactive methods, can be extended to the simultaneous processing of all spectra in the time series using the common information present in the spectra. We show that this approach yields statistically better results than processing the individual signals separately.
使用交互式非线性最小二乘法拟合方法在时域中对个体磁共振波谱（MRS）信号进行量化是可行的，该方法在某些假设下提供最大似然参数估计，或者使用全自动但统计上次优的黑箱方法。在动力学实验中，测量连续MRS谱的时间序列，其中通常存在有关某些信号参数随时间演变的信息。本文的目的是展示交互式方法的一个代表性示例AMARES如何能够利用谱中存在的公共信息扩展到对时间序列中的所有谱进行同步处理。我们表明，这种方法比单独处理各个信号在统计上能产生更好的结果。
