A unified optimization approach for diffusion tensor imaging technique.
A unified optimization approach for diffusion tensor imaging technique.
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DOI：
10.1016/j.neuroimage.2008.10.004
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发表时间：
2009-02-01
期刊：
影响因子：
5.7
通讯作者：
Lin W
中科院分区：
文献类型：
--
作者：
Gao W;Zhu H;Lin W
提出了一种扩散张量成像（DTI）技术的优化方法，旨在改善张量、分数各向异性（FA）和光纤方向的估计。通过模拟退火算法，该方法可以同时优化成像参数（梯度持续时间/分离，读出时间和TE）， b值和扩散梯度方向，无论是否包含张量场的先验知识。此外，优化过程中还考虑了本研究中估计张量的最小二乘方法。蒙特卡罗模拟了三种不同的光纤分布情况，包括光纤定向在1 （CONE1）和3 （CONE3）锥体区域（每个锥体中有50个张量以20°发散角有序定向）和均匀光纤分布（UNIF）。此外，我们测试了三种不同信噪比的成像采集方案，分别为M/N=1/6、2/12和5/30，其中M和N分别为b=0和b>0张图像的个数。结果表明，UNIF的最佳b值范围为0.7 ~ 1.0 × 109 s/m2。但CONE1和CONE3的最优b值范围比UNIF的更高、更宽。此外，与传统方法相比，该方法大大降低了张量的偏差和标准差（SD）以及FA的SD，并提高了光纤方向估计的准确性，特别是在CONE1中。综上所述，所提出的统一优化方法为优化DTI实验提供了一种直接、同步的方法。
An optimization approach for diffusion tensor imaging (DTI) technique is proposed, aiming to improve the estimates of tensors, fractional anisotropy (FA), and fiber directions. With the simulated annealing algorithm, the proposed approach simultaneously optimizes imaging parameters (gradient duration/separation, read-out time, and TE), b-values, and diffusion gradient directions either with or without incorporating prior knowledge of tensor fields. In addition, the method through which tensors are estimated, least squares in our study, was also considered in the optimization procedures. Monte-Carlo simulations were performed for three different scenarios of prior fiber distributions including fibers orientated in 1 (CONE1) and 3 (CONE3) cone areas (50 tensors orderly oriented within a diverging angle of 20° in each cone) and a uniform fiber distribution (UNIF). In addition, three imaging acquisition schemes together with different signal-to-noise ratios were tested, including M/N=1/6, 2/12, and 5/30 for each prior fiber distribution where M and N were the number of b=0 and b>0 images, respectively. Our results show that the optimal b-value ranges between 0.7 and 1.0 × 109 s/m2 for UNIF. However, the optimal b-value ranges become both higher and wider for CONE1 and CONE3 than that of UNIF. In addition, the biases and standard deviations (SD) of tensors, and SD of FA are substantially reduced and the accuracy of fiber directional estimates is improved using the proposed approach particularly in CONE1 when compared with the conventional approaches. Together, the proposed unified optimization approach may offer a direct and simultaneous means to optimize DTI experiments.
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影响因子：
3.3
作者：
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通讯作者：
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影响因子：
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作者：
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发表时间：
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期刊：
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影响因子：
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作者：
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通讯作者：
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