Sun Yat-sen University Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Center on Aging and Health, Johns Hopkins University, Baltimore, MD, USA.
Psychiatry Res Neuroimaging. 2018 Apr 30;274:23-30. doi: 10.1016/j.pscychresns.2018.02.005. Epub 2018 Feb 11.
Total intracranial volume (TIV) is often used as a measure of brain size to correct for individual variability in magnetic resonance imaging (MRI) based morphometric studies. An adjustment of TIV can greatly increase the statistical power of brain morphometry methods. As such, an accurate and precise TIV estimation is of great importance in MRI studies. In this paper, we compared three automated TIV estimation methods (multi-atlas likelihood fusion (MALF), Statistical Parametric Mapping 8 (SPM8) and FreeSurfer (FS)) using longitudinal T1-weighted MR images in a cohort of 70 older participants at elevated sociodemographic risk for Alzheimer's disease. Statistical group comparisons in terms of four different metrics were performed. Furthermore, sex, education level, and intervention status were investigated separately for their impacts on the TIV estimation performance of each method. According to our experimental results, MALF was the least susceptible to atrophy, while SPM8 and FS suffered a loss in precision. In group-wise analysis, MALF was the least sensitive method to group variation, whereas SPM8 was particularly sensitive to sex and FS was unstable with respect to education level. In terms of effectiveness, both MALF and SPM8 delivered a user-friendly performance, while FS was relatively computationally intensive.
全脑容量（TIV）常用于磁共振成像（MRI）形态计量学研究中，以校正大脑大小的个体差异。TIV 的调整可以极大地提高脑形态计量学方法的统计效力。因此，在 MRI 研究中，准确和精确的 TIV 估计非常重要。在本文中，我们比较了三种自动 TIV 估计方法（多图谱似然融合（MALF）、统计参数映射 8（SPM8）和 FreeSurfer（FS）），使用 70 名处于阿尔茨海默病高社会人口风险的老年参与者的纵向 T1 加权 MRI 图像。使用四种不同的指标进行了统计组比较。此外，还分别研究了性别、教育水平和干预状态对每种方法的 TIV 估计性能的影响。根据我们的实验结果，MALF 对萎缩的敏感性最低，而 SPM8 和 FS 则精度降低。在组间分析中，MALF 是对组间变化最不敏感的方法，而 SPM8 对性别特别敏感，FS 则对教育水平不稳定。在有效性方面，MALF 和 SPM8 都具有用户友好的性能，而 FS 相对计算密集。
