icometrix, Research and Development, Leuven, Belgium; Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium.
icometrix, Research and Development, Leuven, Belgium.
Neuroimage Clin. 2018 May 23;19:734-744. doi: 10.1016/j.nicl.2018.05.030. eCollection 2018.
Pediatric brain volumetric analysis based on Magnetic Resonance Imaging (MRI) is of particular interest in order to understand the typical brain development and to characterize neurodevelopmental disorders at an early age. However, it has been shown that the results can be biased due to head motion, inherent to pediatric data, and due to the use of methods based on adult brain data that are not able to accurately model the anatomical disparity of pediatric brains. To overcome these issues, we proposed child, a tool developed for the analysis of pediatric neuroimaging data that uses an age-specific atlas and a probabilistic model-based approach in order to segment the gray matter (GM) and white matter (WM). The tool was extensively validated on 55 scans of children between 5 and 6 years old (including 13 children with developmental dyslexia) and 10 pairs of test-retest scans of children between 6 and 8 years old and compared with two state-of-the-art methods using an adult atlas, namely ico (applying a probabilistic model-based segmentation) and Freesurfer (applying a surface model-based segmentation). The results obtained with child showed a better reproducibility of GM and WM segmentations and a better robustness to head motion in the estimation of GM volume compared to Freesurfer. Evaluated on two subjects, child showed good accuracy with 82-84% overlap with manual segmentation for both GM and WM, thereby outperforming the adult-based methods (icobrain and Freesurfer), especially for the subject with poor quality data. We also demonstrated that the adult-based methods needed double the number of subjects to detect significant morphological differences between dyslexics and typical readers. Once further developed and validated, we believe that child would provide appropriate and reliable measures for the examination of children's brain.
基于磁共振成像（MRI）的儿科脑容量分析对于了解典型的大脑发育和在早期阶段描述神经发育障碍具有特别的意义。然而，已经表明，由于儿科数据固有的头部运动以及由于使用不能准确模拟儿科大脑解剖差异的基于成人脑数据的方法，结果可能存在偏差。为了克服这些问题，我们提出了 child，这是一种为儿科神经影像学数据分析而开发的工具，它使用特定年龄的图谱和基于概率模型的方法来分割灰质（GM）和白质（WM）。该工具在 55 名 5 至 6 岁儿童（包括 13 名发育性阅读障碍儿童）和 10 对 6 至 8 岁儿童的测试 - 复测扫描上进行了广泛验证，并与使用成人图谱的两种最先进的方法（即 ico（应用基于概率模型的分割）和 Freesurfer（应用基于表面模型的分割））进行了比较。与 Freesurfer 相比，child 获得的结果显示 GM 和 WM 分割的可重复性更好，在 GM 体积估计中对头部运动的鲁棒性更好。在两个受试者上进行评估时，child 对 GM 和 WM 的手动分割的准确性较高，达到 82-84%的重叠，从而优于基于成人的方法（icobrain 和 Freesurfer），特别是对于数据质量较差的受试者。我们还证明，基于成人的方法需要两倍的受试者数量才能检测到阅读障碍者和典型读者之间的显著形态差异。一旦进一步开发和验证，我们相信 child 将为检查儿童大脑提供适当和可靠的指标。
