Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK; Department of Computer Science and Centre for Medical Image Computing, University College London, UK.
Penn Image Computing and Science Laboratory (PISCL), Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Neuroimage. 2017 Aug 15;157:675-694. doi: 10.1016/j.neuroimage.2017.04.057. Epub 2017 Apr 27.
Diffusion-weighted imaging (DWI) is becoming an increasingly important tool for studying brain development. DWI analyses relying on manually-drawn regions of interest and tractography using manually-placed waypoints are considered to provide the most accurate characterisation of the underlying brain structure. However, these methods are labour-intensive and become impractical for studies with large cohorts and numerous white matter (WM) tracts. Tract-specific analysis (TSA) is an alternative WM analysis method applicable to large-scale studies that offers potential benefits. TSA produces a skeleton representation of WM tracts and projects the group's diffusion data onto the skeleton for statistical analysis. In this work we evaluate the performance of TSA in analysing preterm infant data against results obtained from native space tractography and tract-based spatial statistics. We evaluate TSA's registration accuracy of WM tracts and assess the agreement between native space data and template space data projected onto WM skeletons, in 12 tracts across 48 preterm neonates. We show that TSA registration provides better WM tract alignment than a previous protocol optimised for neonatal spatial normalisation, and that TSA projects FA values that match well with values derived from native space tractography. We apply TSA for the first time to a preterm neonatal population to study the effects of age at scan on WM tracts around term equivalent age. We demonstrate the effects of age at scan on DTI metrics in commissural, projection and association fibres. We demonstrate the potential of TSA for WM analysis and its suitability for infant studies involving multiple tracts.
扩散加权成像(DWI)正成为研究大脑发育的一种越来越重要的工具。依赖于手动绘制感兴趣区域和手动放置路标进行追踪的 DWI 分析被认为可以最准确地描述潜在的大脑结构。然而,这些方法劳动强度大,对于具有大量队列和众多白质(WM)束的研究变得不切实际。束特异性分析(TSA)是一种替代的 WM 分析方法,适用于大规模研究,具有潜在的益处。TSA 生成 WM 束的骨架表示,并将组的扩散数据投影到骨架上进行统计分析。在这项工作中,我们评估了 TSA 在分析早产儿数据方面的性能,以与从原始空间追踪和基于束的空间统计学获得的结果进行比较。我们评估了 TSA 在 48 名早产儿的 12 个 WM 束中的 WM 束配准准确性,并评估了原始空间数据与投影到 WM 骨架上的模板空间数据之间的一致性。我们表明,TSA 配准提供了比专门针对新生儿空间归一化优化的先前协议更好的 WM 束对齐,并且 TSA 投影的 FA 值与从原始空间追踪术得出的值非常吻合。我们首次将 TSA 应用于早产儿人群,以研究扫描时的年龄对接近足月年龄的 WM 束的影响。我们证明了扫描时的年龄对连合纤维、投射纤维和联合纤维的 DTI 指标的影响。我们展示了 TSA 用于 WM 分析的潜力及其在涉及多个束的婴儿研究中的适用性。