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Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance

机译:精确自动估计颅内总体积:烦扰变量少

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摘要

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2 = 0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4 ± 35.4 ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p < 0.001) than for either SPM8 (R2 = 0.577 CI (0.500, 0.644)) or FreeSurfer (R2 = 0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.
机译:颅内总体积(TIV / ICV)是对大脑和大脑区域进行体积分析的重要协变量,尤其是在神经退行性疾病研究中,可以提供最大的病前大脑体积。黄金标准的方法是手动描述脑部扫描,但这需要训练有素的操作员进行认真的工作。我们评估了用于TIV测量的统计参数映射12(SPM12)自动分段来代替手动分段,并将其与SPM8和FreeSurfer 5.3.0进行了比较。对于在一项多中心阿尔茨海默氏病临床试验中从288名参与者中获得的T1加权MRI,我们发现SPM12 TIV与手动TIV之间存在高度相关性(R 2 = 0.940,95%置信区间(0.924, 0.953)),平均差异很小(SPM12比手册低40.4±35.4 ml,占研究中总体TIV的2.8%)。与SPM8(R 2 = 0.577 CI(0.500,0.644))或SPM12的手动测量(使用TIV作为协变量的关键方面)的相关性显着更高(p <0.001) FreeSurfer(R 2 = 0.801 CI(0.744,0.843))。这些结果表明,即使在多个中心具有挑战性的情况下以及存在神经退行性病变的情况下,SPM12 TIV估计值仍可以替代劳动密集型人工估计值。我们还简要讨论了针对TIV进行调整的统计建模方法的某些方面。

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