Plaisier A, Pieterman K, Lequin M H, Govaert P, Heemskerk A M, Reiss I K M, Krestin G P, Leemans A, Dudink J
From the Division of Neonatology, Department of Pediatrics (A.P., K.P., P.G., A.M.H., J.D.), Erasmus Medical Center-Sophia, Rotterdam, The NetherlandsDepartments of Radiology (A.P., M.H.L., A.M.H., G.P.K., J.D.).
From the Division of Neonatology, Department of Pediatrics (A.P., K.P., P.G., A.M.H., J.D.), Erasmus Medical Center-Sophia, Rotterdam, The Netherlands.
AJNR Am J Neuroradiol. 2014 Jun;35(6):1219-25. doi: 10.3174/ajnr.A3830. Epub 2014 Jan 9.
Neonatal DTI enables quantitative assessment of microstructural brain properties. Although its use is increasing, it is not widely known that vast differences in tractography results can occur, depending on the diffusion tensor estimation methodology used. Current clinical work appears to be insufficiently focused on data quality and processing of neonatal DTI. To raise awareness about this important processing step, we investigated tractography reconstructions of the fornix with the use of several estimation techniques. We hypothesized that the method of tensor estimation significantly affects DTI tractography results.
Twenty-eight DTI scans of infants born <29 weeks of gestation, acquired at 30-week postmenstrual age and without intracranial injury observed, were prospectively collected. Four diffusion tensor estimation methods were applied: 1) linear least squares; 2) weighted linear least squares; 3) nonlinear least squares, and 4) robust estimation of tensors by outlier rejection. Quality of DTI data and tractography results were evaluated for each method.
With nonlinear least squares and robust estimation of tensors by outlier rejection, significantly lower mean fractional anisotropy values were obtained than with linear least squares and weighted linear least squares. Visualized quality of tract reconstruction was significantly higher by use of robust estimation of tensors by outlier rejection and correlated with quality of DTI data.
Quality assessment and choice of processing methodology have considerable impact on neonatal DTI analysis. Dedicated acquisition, quality assessment, and advanced processing of neonatal DTI data must be ensured before performing clinical analyses, such as associating microstructural brain properties with patient outcome.
新生儿弥散张量成像（DTI）能够对脑微观结构特性进行定量评估。尽管其应用日益广泛，但鲜为人知的是，根据所使用的扩散张量估计方法不同，纤维束成像结果可能会出现巨大差异。目前的临床工作似乎对新生儿DTI的数据质量和处理关注不足。为提高对这一重要处理步骤的认识，我们使用多种估计技术研究了穹窿的纤维束成像重建。我们假设张量估计方法会显著影响DTI纤维束成像结果。
前瞻性收集了28例孕龄小于29周的婴儿在孕30周龄时进行的DTI扫描图像，且未观察到颅内损伤。应用了四种扩散张量估计方法：1）线性最小二乘法；2）加权线性最小二乘法；3）非线性最小二乘法；4）通过剔除异常值进行张量的稳健估计。对每种方法的DTI数据质量和纤维束成像结果进行评估。
与线性最小二乘法和加权线性最小二乘法相比，使用非线性最小二乘法和通过剔除异常值进行张量的稳健估计时，平均各向异性分数值显著更低。通过使用通过剔除异常值进行张量的稳健估计，纤维束重建的可视化质量显著更高，且与DTI数据质量相关。
质量评估和处理方法的选择对新生儿DTI分析有相当大的影响。在进行临床分析（如将脑微观结构特性与患者预后相关联）之前，必须确保新生儿DTI数据的专门采集、质量评估和先进处理。
