Medical Research Council/University of Cape Town Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Observatory 7935, South Africa.
IEEE Trans Biomed Eng. 2010 Jun;57(6):1377-87. doi: 10.1109/TBME.2009.2038667. Epub 2010 Feb 17.
Near infrared spectroscopy (NIRS) is rapidly gaining popularity for functional brain imaging. It is well suited to studies of patients or children; however, in these populations particularly, motion artifacts can present a problem. Here, we propose the use of imaging channels with negligible distance between light source and detector to detect subject motion, without the need for an additional motion sensor. Datasets containing deliberate motion artifacts were obtained from three subjects. Motion artifacts could be detected in the signal from the co-located channels with a minimum sensitivity of 0.75 and specificity of 0.98. Five techniques for removing motion artifact from the functional signals were compared, namely two-input recursive least squares (RLS) adaptive filtering, wavelet-based filtering, independent component analysis (ICA), and two-channel and multiple-channel regression. In most datasets, the median change in SNR across all channels was the greatest using ICA or multiple-channel regression. RLS adaptive filtering produced the smallest increase in SNR. Where sharp spikes were present, wavelet filtering produced the largest SNR increase. ICA and multiple-channel regression are promising ways to reduce motion artifact in functional NIRS without requiring time-consuming manual techniques.
近红外光谱（NIRS）技术在功能脑成像方面的应用越来越受到关注。它非常适合用于研究患者或儿童；然而，在这些人群中，运动伪影可能是一个问题。在这里，我们提出使用光源和探测器之间距离可忽略不计的成像通道来检测受试者的运动，而无需额外的运动传感器。从三个对象中获得包含故意运动伪影的数据集。可以在共定位通道的信号中检测到运动伪影，其灵敏度最小为 0.75，特异性为 0.98。比较了五种从功能信号中去除运动伪影的技术，即双输入递归最小二乘（RLS）自适应滤波、基于小波的滤波、独立成分分析（ICA）以及双通道和多通道回归。在大多数数据集，ICA 或多通道回归的 SNR 平均变化最大。RLS 自适应滤波产生的 SNR 增加最小。在存在尖锐尖峰的情况下，小波滤波产生的 SNR 增加最大。ICA 和多通道回归是一种有前途的方法，可以在不使用耗时的手动技术的情况下减少功能 NIRS 中的运动伪影。
