Impact of acquisition protocols and processing streams on tissue segmentation of TI weighted MR images
Impact of acquisition protocols and processing streams on tissue segmentation of TI weighted MR images
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DOI：
10.1016/j.neuroimage.2005.07.035
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发表时间：
2006-01-01
期刊：
影响因子：
5.7
通讯作者：
Mazziotta, JC
中科院分区：
文献类型：
--
作者：
Clark, KA;Woods, RP;Mazziotta, JC
将T1加权图像分割为灰质（GM）、白色物质（WM）和脑脊液（CSF）是神经成像中的基本处理步骤，其结果影响许多其他结构成像分析。分割过程中的变异性可能会降低研究检测解剖差异的能力，最大限度地减少此类变异性可以获得更稳健的结果。本文概述了一种简单的策略，可用于（1）选择更优的数据采集和处理协议，（2）量化这种优化的影响。使用这种方法对单个受试者进行多次扫描，我们发现分割算法的选择对变异性的影响最大，而脉冲序列的选择对变异性的影响次之。数据表明，GM的分类是最可变的，并且最佳方案在组织类型之间可能不同。因此，分割数据的预期用途应该在优化中发挥作用。提供的例子来证明，变异性的最小化是不够的优化，还必须考虑的方法的整体精度。简单的体积计算，以说明优化的潜在收益，这些结果表明，从最佳途径的体积估计平均三倍少的变量比从次优途径的估计。因此，这里说明的简单策略可以应用于许多研究，以优化组织分割，这将导致结构神经影像学研究的能力的净增加。(c)2005年爱思唯尔公司All rights reserved.
The segmentation of T1-weighted images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is a fundamental processing step in neuroimaging, the results of which affect many other structural imaging analyses. Variability in the segmentation process can decrease the power of a study to detect anatomical differences, and minimizing such variability can lead to more robust results. This paper outlines a straightforward strategy that can be used (1) to select more optimal data acquisition and processing protocols and (2) to quantify the impact of such optimization. Using this approach with multiple scans of a single subject, we found that the choice of a segmentation algorithm had the largest impact on variability, while the choice of a pulse sequence had the second largest impact. The data indicate that the classification of GM is the most variable, and that the optimal protocol may differ across tissue types. Therefore, the intended use of segmentation data should play a role in optimization. Examples are provided to demonstrate that the minimization of variability is not sufficient for optimization; the overall accuracy of the approach must also be considered. Simple volumetric computations are included to illustrate the potential gain of optimization; these results show that volume estimates from optimal pathways were on average three times less variable than estimates from suboptimal pathways. Therefore, the simple strategy illustrated here can be applied to many studies to optimize tissue segmentation, which should lead to a net increase in the power of structural neuroimaging studies. (c) 2005 Elsevier Inc. All rights reserved.