From the Department of Neurology and Neurobiology of Aging (J.K., M.S., K. Shima, M.N.-S., K. Sakai, T.H., K.O., M.Y.), Kanazawa University Graduate School of Medical Sciences, Takara-machi, Ishikawa, Japan.
Department of Clinical Research (I.M.), Medical and Pharmacological Research Center Foundation, Hakui, Ishikawa, Japan
AJNR Am J Neuroradiol. 2018 Mar;39(3):473-478. doi: 10.3174/ajnr.A5509. Epub 2018 Feb 1.
Although Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) has been introduced as an alternative to conventional voxel-based morphometry, there are scant data available regarding the optimal image-processing settings. The aim of this study was to optimize image-processing and ROI settings for the diagnosis of Alzheimer disease using DARTEL.
Between May 2002 and August 2014, we selected 158 patients with Alzheimer disease and 198 age-matched healthy subjects; 158 healthy subjects served as the control group against the patients with Alzheimer disease, and the remaining 40 served as the healthy data base. Structural MR images were obtained in all the participants and were processed using DARTEL-based voxel-based morphometry with a variety of settings. These included modulated or nonmodulated, nonsmoothed or smoothed settings with a 4-, 8-, 12-, 16-, or 20-mm kernel size. A score was calculated for each ROI, and univariate and multivariate logistic regression analyses were performed to determine the optimal ROI settings for each dataset. The optimal settings were defined as those demonstrating the highest χ test statistics in the multivariate logistic regression analyses. Finally, using the optimal settings, we obtained receiver operating characteristic curves. The models were verified using 10-fold cross-validation.
The optimal settings were obtained using the hippocampus and precuneus as ROIs without modulation and smoothing. The average area under the curve was 0.845 (95% confidence interval, 0.788-0.902).
We recommend using the precuneus and hippocampus as ROIs without modulation and smoothing for DARTEL-based voxel-based morphometry as a tool for diagnosing Alzheimer disease.
尽管基于指数李代数的可变形解剖配准（DARTEL）已被引入作为传统体素形态计量学的替代方法，但关于最佳图像处理设置的数据很少。本研究的目的是优化 DARTEL 诊断阿尔茨海默病的图像处理和 ROI 设置。
2002 年 5 月至 2014 年 8 月期间，我们选择了 158 例阿尔茨海默病患者和 198 名年龄匹配的健康受试者；158 名健康受试者作为对照组与阿尔茨海默病患者进行比较，其余 40 名作为健康数据库。所有参与者均获得结构磁共振图像，并使用基于 DARTEL 的体素形态计量学进行处理，设置有多种调制或非调制、平滑或非平滑、核大小为 4、8、12、16 或 20mm。为每个 ROI 计算得分，并进行单变量和多变量逻辑回归分析，以确定每个数据集的最佳 ROI 设置。最佳设置定义为在多变量逻辑回归分析中χ检验统计量最高的设置。最后，使用最佳设置，我们获得了接收者操作特征曲线。使用 10 倍交叉验证验证模型。
使用未调制和未平滑的海马体和楔前叶作为 ROI 获得了最佳设置。平均曲线下面积为 0.845（95%置信区间，0.788-0.902）。
我们建议使用未调制和未平滑的楔前叶和海马体作为 ROI，用于基于 DARTEL 的体素形态计量学作为诊断阿尔茨海默病的工具。
