Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts
Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts
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DOI：
10.1016/j.mri.2008.01.034
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发表时间：
2008-07-01
影响因子：
2.5
通讯作者：
Mori, Susumu
中科院分区：
文献类型：
--
作者：
Landman, Bennett A.;Farrell, Jonathan A. D.;Mori, Susumu
扩散张量成像（DTI）提供方向扩散率的测量，并已广泛用于表征大脑组织微结构的变化。 DTI 在大脑以外的应用中越来越受到重视，其中分辨率、运动和短 T-2 值通常限制可实现的信噪比 (SNR)。因此，重新审视低信噪比条件下的张量估计主题非常重要。开发了一个理论框架来对 DTI 中的噪声进行建模，并通过基于该理论的模拟，阐明了噪声、张量估计方法和采集协议对张量导出量（例如分数各向异性和表观扩散系数）的影响程度。然后根据临床数据验证这些结果。结果表明，紧张对比的可靠性取决于噪声水平、估计方法、扩散加权方案和基础解剖结构。偏差和错误的倾向并不随着噪声单调增加。比较结果以图形和表格形式显示，因此可以根据具体情况做出有关合适采集协议和处理方法的决定，而无需进行详尽的实验。 (C) 2008 Elsevier Inc. 保留所有权利。
Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in the tissue microarchitecture of the brain. DTI is gaining prominence in applications outside of the brain, where resolution, motion and short T-2 values often limit the achievable signal-to-noise ratio (SNR). Consequently, it is important to revisit the topic of tensor estimation in low-SNR regimes. A theoretical framework is developed to model noise in DTI, and by using simulations based on this theory, the degree to which the noise, tensor estimation method and acquisition protocol affect tensor-derived quantities, such as fractional anisotropy and apparent diffusion coefficient, is clarified. These results are then validated against clinical data. It is shown that reliability of tenser contrasts depends on the noise level, estimation method, diffusion-weighting scheme and underlying anatomy. The propensity for bias and errors does not monotonically increase with noise. Comparative results are shown in both graphical and tabular forms, so that decisions about suitable acquisition protocols and processing methods can be made on a case-by-case basis without exhaustive experimentation. (C) 2008 Elsevier Inc. All rights reserved.