Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography.
Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography.
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DOI：
10.3389/fnene.2010.00014
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发表时间：
2010-01-01
期刊：
影响因子：
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通讯作者：
Culver, Joseph P
中科院分区：
文献类型：
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作者：
Gregg, Nicholas M;White, Brian R;Culver, Joseph P
功能近红外光谱技术是一种便携式脑血流动力学监测仪，具有广泛的临床应用前景。然而，在fNIRS中，来自大脑的血管信号经常被头皮和颅骨中存在的血管信号所掩盖。在本文中，我们评估了通过使用高密度漫射光学断层扫描（DOT）改善成年受试者体内数据的两种方法。首先，我们测试我们是否可以扩展肤浅的回归方法（利用多个源探测器对分离）从稀疏光电二极管阵列的应用与DOT成像阵列。为了实现这一目标，我们修改的方法，从更深的采样通道，使用浅测量的平均值，以消除生理伪影。其次，DOT提供三维图像重建，并且应该明确分离不同的组织层。我们测试DOT的深度切片是否可以完全去除浅表生理伪影。在这里，我们评估的信号质量和再现性的改善，由于这些方法使用一个良好的特征化的视觉范例和我们的高密度DOT系统。这两种方法都可以消除数据中的噪声，从而获得更清晰的成像和更一致的血流动力学反应。此外，这两种方法协同作用，当这些方法一起使用时会有更大的改进。
Functional near infrared spectroscopy (fNIRS) is a portable monitor of cerebral hemodynamics with wide clinical potential. However, in fNIRS, the vascular signal from the brain is often obscured by vascular signals present in the scalp and skull. In this paper, we evaluate two methods for improving in vivo data from adult human subjects through the use of high-density diffuse optical tomography (DOT). First, we test whether we can extend superficial regression methods (which utilize the multiple source-detector pair separations) from sparse optode arrays to application with DOT imaging arrays. In order to accomplish this goal, we modify the method to remove physiological artifacts from deeper sampling channels using an average of shallow measurements. Second, DOT provides three-dimensional image reconstructions and should explicitly separate different tissue layers. We test whether DOT's depth-sectioning can completely remove superficial physiological artifacts. Herein, we assess improvements in signal quality and reproducibility due to these methods using a well-characterized visual paradigm and our high-density DOT system. Both approaches remove noise from the data, resulting in cleaner imaging and more consistent hemodynamic responses. Additionally, the two methods act synergistically, with greater improvements when the approaches are used together.