论文标题
新型T2弛豫分析方法相同ECOS：通过实验条件模拟的多个指数分析的频谱分析
Introduction to a novel T2 relaxation analysis method SAME-ECOS: Spectrum Analysis for Multiple Exponentials via Experimental Condition Oriented Simulation
论文作者
论文摘要
我们提出了一种新型的T2弛豫数据分析方法，我们通过实验条件为导向的模拟（Same-ECOS）命名了多个指数的频谱分析。基于信息理论和机器学习神经网络算法的组合开发的相同ECO是针对不同的MR实验条件量身定制的，将多指数衰减数据分解为T2 Spectra，该数据被认为是使用常规拟合算法（包括常见的非管理性最小型Squares（NNLS））的方法。我们的结果表明，与NNL相比，模拟衍生的相同模型在较短的时间内产生了更可靠的T2光谱，从而增加了临床环境中多组分T2衰减分析的可行性。
We propose a novel T2 relaxation data analysis method which we have named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME-ECOS). SAME-ECOS, which was developed based on a combination of information theory and machine learning neural network algorithms, is tailored for different MR experimental conditions, decomposing multi-exponential decay data into T2 spectra, which had been considered an ill-posed problem using conventional fitting algorithms, including the commonly used non-negative least squares (NNLS) method. Our results demonstrated that, compared with NNLS, the simulation-derived SAME-ECOS model yields much more reliable T2 spectra in a dramatically shorter time, increasing the feasibility of multi-component T2 decay analysis in clinical settings.
