Booth Brian G, Miller Steven P, Brown Colin J, Poskitt Kenneth J, Chau Vann, Grunau Ruth E, Synnes Anne R, Hamarneh Ghassan
Simon Fraser University, Burnaby, Canada.
The Hospital for Sick Children, Toronto, Canada; University of British Columbia, Vancouver, Canada; University of Toronto, Toronto, Canada.
Neuroimage. 2016 Jan 15;125:705-723. doi: 10.1016/j.neuroimage.2015.08.079. Epub 2015 Oct 26.
We introduce the STEAM DTI analysis engine: a whole brain voxel-based analysis technique for the examination of diffusion tensor images (DTIs). Our STEAM analysis technique consists of two parts. First, we introduce a collection of statistical templates that represent the distribution of DTIs for a normative population. These templates include various diffusion measures from the full tensor, to fractional anisotropy, to 12 other tensor features. Second, we propose a voxel-based analysis (VBA) pipeline that is reliable enough to identify areas in individual DTI scans that differ significantly from the normative group represented in the STEAM statistical templates. We identify and justify choices in the VBA pipeline relating to multiple comparison correction, image smoothing, and dealing with non-normally distributed data. Finally, we provide a proof of concept for the utility of STEAM on a cohort of 134 very preterm infants. We generated templates from scans of 55 very preterm infants whose T1 MRI scans show no abnormalities and who have normal neurodevelopmental outcome. The remaining 79 infants were then compared to the templates using our VBA technique. We show: (a) that our statistical templates display the white matter development expected over the modeled time period, and (b) that our VBA results detect abnormalities in the diffusion measurements that relate significantly with both the presence of white matter lesions and with neurodevelopmental outcomes at 18months. Most notably, we show that STEAM produces personalized results while also being able to highlight abnormalities across the whole brain and at the scale of individual voxels. While we show the value of STEAM on DTI scans from a preterm infant cohort, STEAM can be equally applied to other cohorts as well. To facilitate this whole-brain personalized DTI analysis, we made STEAM publicly available at http://www.sfu.ca/bgb2/steam.
我们介绍了STEAM DTI分析引擎：一种基于全脑体素的分析技术，用于检查扩散张量图像（DTI）。我们的STEAM分析技术由两部分组成。首先，我们引入了一组统计模板，这些模板代表了正常人群DTI的分布。这些模板包括从完整张量到分数各向异性，再到其他12个张量特征的各种扩散测量值。其次，我们提出了一种基于体素的分析（VBA）流程，该流程足够可靠，能够识别个体DTI扫描中与STEAM统计模板所代表的正常组有显著差异的区域。我们确定并说明了VBA流程中与多重比较校正、图像平滑以及处理非正态分布数据相关的选择。最后，我们在一组134名极早产儿中为STEAM的实用性提供了概念验证。我们从55名极早产儿的扫描中生成了模板，这些早产儿的T1 MRI扫描未显示异常且神经发育结果正常。然后使用我们的VBA技术将其余79名婴儿与模板进行比较。我们表明：（a）我们的统计模板显示了在建模时间段内预期的白质发育情况，以及（b）我们的VBA结果检测到扩散测量中的异常，这些异常与白质病变的存在以及18个月时的神经发育结果均有显著关联。最值得注意的是，我们表明STEAM既能产生个性化结果，又能在全脑范围内和个体体素尺度上突出异常。虽然我们展示了STEAM在极早产儿队列的DTI扫描中的价值，但STEAM同样可以应用于其他队列。为了促进这种全脑个性化DTI分析，我们将STEAM公开提供在http://www.sfu.ca/bgb2/steam 。
