Topographic localization of brain activation in diffuse optical imaging using spherical wavelets
Topographic localization of brain activation in diffuse optical imaging using spherical wavelets
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DOI：
10.1088/0031-9155/54/20/023
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发表时间：
2009-10-21
影响因子：
3.5
通讯作者：
Huppert, T. J.
中科院分区：
文献类型：
--
作者：
Abdelnour, F.;Schmidt, B.;Huppert, T. J.
漫射光学成像是一种非侵入性技术，它利用近红外光通过放置在头部表面的一系列传感器来测量大脑活动的变化。与功能性MRI相比，光学成像具有便携的优点，同时能够以高时间分辨率记录大脑内氧和脱氧血红蛋白的功能变化。然而，从光学测量中重建精确的脑活动空间图像代表了一个病态和不确定的问题，需要正则化。这些重建得益于整合了关于大脑潜在空间结构和功能的先验信息。在这项工作中，我们描述了一种新的图像重建方法，该方法使用来自结构MRI的基于表面的小波来整合关于大脑的高分辨率解剖和结构先验信息。这种基于表面的方法通过重建和呈现大脑激活的地形图（二维）直接在皮层的折叠表面上，来近似大脑的激活模式。通过截断奇异值分解对光学正演模型的小波投影进行伪反演，直接估计小波系数集。我们使用基于Shannon熵的重构度量来量化小波系数的稀疏负荷，并用于确定该逆模型的最优截断和正则化。在这项工作中，该模型的性能举例说明了几个案例的数值模拟和实验数据与功能磁共振成像的比较。
Diffuse optical imaging is a non-invasive technique that uses near-infrared light to measure changes in brain activity through an array of sensors placed on the surface of the head. Compared to functional MRI, optical imaging has the advantage of being portable while offering the ability to record functional changes in both oxy-and deoxy-hemoglobin within the brain at a high temporal resolution. However, the reconstruction of accurate spatial images of brain activity from optical measurements represents an ill-posed and underdetermined problem that requires regularization. These reconstructions benefit from incorporating prior information about the underlying spatial structure and function of the brain. In this work, we describe a novel image reconstruction approach which uses surface-based wavelets derived from structural MRI to incorporate high-resolution anatomical and structural prior information about the brain. This surface-based approach is used to approximate brain activation patterns through the reconstruction and presentation of topographical (two-dimensional) maps of brain activation directly onto the folded surface of the cortex. The set of wavelet coefficients is directly estimated by a truncated singular-value decomposition based pseudo-inversion of the wavelet projection of the optical forward model. We use a reconstruction metric based on Shannon entropy which quantifies the sparse loading of the wavelet coefficients and is used to determine the optimal truncation and regularization of this inverse model. In this work, examples of the performance of this model are illustrated for several cases of numerical simulation and experimental data with comparison to functional magnetic resonance imaging.