论文标题
用于优化和评估T1和T2弛豫方法MRI的统计框架
A Statistical Framework for Optimizing and Evaluating MRI of T1 and T2 Relaxometry Approaches
论文作者
论文摘要
本文提出了一个统计框架，以优化和评估MR参数$ t_1 $和$ t_2 $映射功能，用于定量MRI松弛计方法。该分析探讨了每单位扫描时间的内在MR参数估计精度，称为$ t_ {1,2} $ - noise比率（TNR）效率，对于不同的生物学上现实的放松时间。 TNR效率是根据CRAMER-RAO BOND（CRB）定义的，这是参数估算方差的统计下限。几何解释新的TNR效率定义，揭示了一个更完整的模型，描述了控制$ t_1 $/$ t_2 $映射功能的因素。本文比较了$ T_1 $映射方法，包括倒置恢复（IR）家庭序列以及Look-Locker（LL）序列和同时$ T_1 $和$ T_2 $映射方法，包括Spin-Echo倒置恢复（SEIR）（SEIR）和驱动的平衡单脉冲观察和$ T_1 $/$ T_2 $（DESCOT）的单个脉冲观察。优化所有脉冲参数以最大化不同$ T_1 $和$ T_2 $范围的TNR效率。非线性最小平方估计（NLSE）为$ t_1 $/$ t_2 $的蒙特卡洛模拟验证了估算器性能的理论预测。
This paper proposes a statistical framework to optimize and evaluate the MR parameter $T_1$ and $T_2$ mapping capabilities for quantitative MRI relaxometry approaches. This analysis explores the intrinsic MR parameter estimate precision per unit scan time, termed the $T_{1,2}$-to-noise ratio (TNR) efficiency, for different ranges of biologically realistic relaxation times. The TNR efficiency is defined in terms of the Cramer-Rao bound (CRB), a statistical lower bound on the parameter estimate variance. Geometrically interpreting the new TNR efficiency definition reveals a more complete model describing the factors controlling the $T_1$/$T_2$ mapping capabilities. This paper compares $T_1$ mapping approaches including the inversion recovery (IR) family sequences and the Look-Locker (LL) sequence and simultaneous $T_1$ and $T_2$ mapping approaches including the spin-echo inversion recovery (SEIR) and driven equilibrium single pulse observation of $T_1$/$T_2$ (DESPOT) sequences. All pulse parameters are optimized to maximize the TNR efficiency within different $T_1$ and $T_2$ ranges of interest. Monte Carlo simulations with non-linear least square estimation (NLSE) of $T_1$/$T_2$ validated the theoretical predictions on the estimator performances.
