Müller Karsten, Lohmann Gabriele, Zysset Stefan, von Cramon D Yves
Max Planck Institute of Cognitive Neuroscience, Leipzig, Germany.
J Magn Reson Imaging. 2003 Jan;17(1):20-30. doi: 10.1002/jmri.10219.
To improve the signal-to-noise ratio (SNR) of functional magnetic resonance imaging (fMRI) data, an approach is developed that combines wavelet-based methods with the general linear model.
Ruttimann et al. (1) developed a wavelet-based statistical procedure to test wavelet-space partitions for significant wavelet coefficients. Their method is applicable for the detection of differences between images acquired under two experimental conditions using long blocks of stimulation. However, many neuropsychological questions require more complicated event-related paradigms and more experimental conditions. Therefore, in order to apply wavelet-based methods to a wide range of experiments, we present a new approach that is based on the general linear model and wavelet thresholding.
In contrast to a monoresolution filter, the application of the wavelet method increased the SNR and showed a set of clearly dissociable activations. Furthermore, no relevant decrease of the local maxima was observed.
Wavelet-based methods can increase the SNR without diminishing the signal amplitude, while preserving the spatial resolution of the image. The anatomical localization is strongly improved.
为提高功能磁共振成像（fMRI）数据的信噪比（SNR），开发了一种将基于小波的方法与通用线性模型相结合的方法。
鲁蒂曼等人（1）开发了一种基于小波的统计程序，用于测试小波空间分区中显著的小波系数。他们的方法适用于检测在两种实验条件下使用长时间刺激块采集的图像之间的差异。然而，许多神经心理学问题需要更复杂的事件相关范式和更多的实验条件。因此，为了将基于小波的方法应用于广泛的实验，我们提出了一种基于通用线性模型和小波阈值处理的新方法。
与单分辨率滤波器相比，小波方法的应用提高了信噪比，并显示出一组明显可分离的激活。此外，未观察到局部最大值有相关下降。
基于小波的方法可以在不减小信号幅度的情况下提高信噪比，同时保持图像的空间分辨率。解剖定位得到了显著改善。
