Van De Ville Dimitri, Seghier Mohamed L, Lazeyras François, Blu Thierry, Unser Michael
Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), and Department of Radiology and Medical Informatics, University Hospital Geneva, Switzerland.
Neuroimage. 2007 Oct 1;37(4):1205-17. doi: 10.1016/j.neuroimage.2007.06.011. Epub 2007 Jun 19.
Recently, we have introduced an integrated framework that combines wavelet-based processing with statistical testing in the spatial domain. In this paper, we propose two important enhancements of the framework. First, we revisit the underlying paradigm; i.e., that the effect of the wavelet processing can be considered as an adaptive denoising step to "improve" the parameter map, followed by a statistical detection procedure that takes into account the non-linear processing of the data. With an appropriate modification of the framework, we show that it is possible to reduce the spatial bias of the method with respect to the best linear estimate, providing conservative results that are closer to the original data. Second, we propose an extension of our earlier technique that compensates for the lack of shift-invariance of the wavelet transform. We demonstrate experimentally that both enhancements have a positive effect on performance. In particular, we present a reproducibility study for multi-session data that compares WSPM against SPM with different amounts of smoothing. The full approach is available as a toolbox, named WSPM, for the SPM2 software; it takes advantage of multiple options and features of SPM such as the general linear model.
最近，我们引入了一个集成框架，该框架将基于小波的处理与空间域中的统计测试相结合。在本文中，我们提出了该框架的两个重要改进。首先，我们重新审视了基础范式；即，小波处理的效果可以被视为一个自适应去噪步骤，以“改善”参数图，随后是一个考虑数据非线性处理的统计检测程序。通过对框架进行适当修改，我们表明相对于最佳线性估计，可以减少该方法的空间偏差，提供更接近原始数据的保守结果。其次，我们提出了对我们早期技术的扩展，以补偿小波变换缺乏平移不变性的问题。我们通过实验证明这两个改进对性能都有积极影响。特别是，我们针对多会话数据进行了一项再现性研究，将小波空间处理方法（WSPM）与具有不同平滑量的统计参数映射（SPM）进行了比较。完整的方法作为一个名为WSPM的工具箱提供给SPM2软件；它利用了SPM的多个选项和功能，如通用线性模型。
