Within-subject template estimation for unbiased longitudinal image analysis.
Within-subject template estimation for unbiased longitudinal image analysis.
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DOI：
10.1016/j.neuroimage.2012.02.084
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发表时间：
2012-07-16
期刊：
影响因子：
5.7
通讯作者：
Fischl, Bruce
中科院分区：
文献类型：
--
作者：
Reuter, Martin;Schmansky, Nicholas J.;Rosas, H. Diana;Fischl, Bruce
关键词：
纵向图像分析在正常衰老和神经退行性疾病的临床研究中变得越来越重要。此外，人们越来越认识到纵向获取的结构图像以及可靠的图像处理在评估疾病修饰疗法方面的潜在用途。挑战涉及现有横断面处理工具固有的变异性、纵向处理中偏差的引入以及潜在的过度正则化。在本文中，我们介绍一种新的纵向图像处理框架，它基于无偏、稳健的个体内模板创建，用于任意多个时间点的脑部MRI的自动表面重建和分割。我们证明，对所有输入图像进行完全相同的处理是至关重要的，因为仅仅消除插值不对称性不足以消除处理偏差。我们通过利用来自个体模板的共同信息初始化每个时间点的处理，成功地减少了变异性并避免了过度正则化。所呈现的结果表明，在保持检测大的解剖偏差能力的同时，精度和判别能力显著提高；因此，它们在临床应用中具有巨大潜力，例如允许更小的样本量或更短的试验来确定疾病特异性生物标志物或量化药物效果。
Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects.
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