Matteau-Pelletier Carl, Dehaes Mathieu, Lesage Frédéric, Lina Jean-Marc
Département de Génie Electrique and Institut de Génie Biomédical, Ecole Polytechnique de Montréal, Montréal, QC, H3C 3A7 Canada.
IEEE Trans Med Imaging. 2009 Mar;28(3):415-22. doi: 10.1109/TMI.2008.2006524.
In diffuse optical imaging (DOI) data analysis, the functional response is contaminated with physiological noise as in functional magnetic resonance imaging (fMRI). In this work we extend a previously proposed method for fMRI to estimate the parameters of a linear model of DOI time series. The regression is performed in the wavelet domain to infer drift coefficients at different scales and to estimate the strength of the hemodynamic response function (HRF). This multiresolution approach benefits from the whitening property of the discrete wavelet transform (DWT), which approximately decorrelates long-memory noise processes. We also show that a more accurate estimation is obtained by removing some regressors correlating with the protocol. Moreover, we observe that this improvement is related to a quantitative measure of 1/f noise. The performances of the method are first evaluated against a standard spline-cosine drift approach with simulated HRF and real background physiology. Lastly, the technique is applied to experimental event-related data acquired by near-infrared spectroscopy (NIRS).
在扩散光学成像（DOI）数据分析中，与功能磁共振成像（fMRI）一样，功能响应会受到生理噪声的干扰。在这项工作中，我们扩展了先前提出的一种用于fMRI的方法，以估计DOI时间序列线性模型的参数。在小波域中进行回归，以推断不同尺度下的漂移系数，并估计血液动力学响应函数（HRF）的强度。这种多分辨率方法受益于离散小波变换（DWT）的白化特性，该特性可使长记忆噪声过程近似去相关。我们还表明，通过去除一些与协议相关的回归变量可以获得更准确的估计。此外，我们观察到这种改进与1/f噪声的定量测量有关。该方法的性能首先通过使用模拟HRF和真实背景生理学数据与标准样条余弦漂移方法进行对比评估。最后，将该技术应用于通过近红外光谱（NIRS）获取的实验事件相关数据。
