Sadeghi Neda, Fletcher P Thomas, Prastawa Marcel, Gilmore John H, Gerig Guido
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):33-40. doi: 10.1007/978-3-319-10443-0_5.
The term prediction implies expected outcome in the future, often based on a model and statistical inference. Longitudinal imaging studies offer the possibility to model temporal change trajectories of anatomy across populations of subjects. In the spirit of subject-specific analysis, such normative models can then be used to compare data from new subjects to the norm and to study progression of disease or to predict outcome. This paper follows a statistical inference approach and presents a framework for prediction of future observations based on past measurements and population statistics. We describe prediction in the context of nonlinear mixed effects modeling (NLME) where the full reference population's statistics (estimated fixed effects, variance-covariance of random effects, variance of noise) is used along with the individual's available observations to predict its trajectory. The proposed methodology is generic in regard to application domains. Here, we demonstrate analysis of early infant brain maturation from longitudinal DTI with up to three time points. Growth as observed in DTI-derived scalar invariants is modeled with a parametric function, its parameters being input to NLME population modeling. Trajectories of new subject's data are estimated when using no observation, only the first or the first two time points. Leave-one-out experiments result in statistics on differences between actual and predicted observations. We also simulate a clinical scenario of prediction on multiple categories, where trajectories predicted from multiple models are classified based on maximum likelihood criteria.
“预测”一词意味着未来的预期结果，通常基于模型和统计推断。纵向成像研究提供了对不同受试者群体的解剖结构随时间变化轨迹进行建模的可能性。本着针对个体分析的精神，这样的规范模型随后可用于将新受试者的数据与正常数据进行比较，以研究疾病进展或预测结果。本文采用统计推断方法，提出了一个基于过去测量值和总体统计数据预测未来观测值的框架。我们在非线性混合效应建模（NLME）的背景下描述预测，其中使用整个参考人群的统计数据（估计的固定效应、随机效应的方差协方差、噪声方差）以及个体的可用观测值来预测其轨迹。所提出的方法在应用领域方面具有通用性。在此，我们展示了对具有多达三个时间点的纵向扩散张量成像（DTI）进行早期婴儿脑成熟度分析。用参数函数对DTI衍生的标量不变量中观察到的生长进行建模，其参数被输入到NLME总体建模中。在不使用观测值、仅使用第一个或前两个时间点时估计新受试者数据的轨迹。留一法实验得出实际观测值与预测观测值之间差异的统计数据。我们还模拟了一个对多个类别进行预测的临床场景，其中根据最大似然标准对从多个模型预测的轨迹进行分类。
