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Optimization of probe geometry for diffuse optical brain imaging based on measurement density and distribution

机译：基于测量密度和分布的漫射光学脑成像探头几何形状的优化

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Optode geometry plays an important role in achieving both good spatial resolution and spatial uniformity of detection in diffuse-optical-imaging-based brain activation studies. The quality of reconstructed images for six optode geometries were studied and compared using a laboratory tissue phantom model that contained an embedded object at two separate locations. The number of overlapping measurements per pixel (i.e., the measurement density) and their spatial distributions were quantified for all six geometries and were correlated with the quality of the resulting reconstructed images. The latter were expressed by the area ratio (AR) and contrast-to-noise ratio (CNR) between reconstructed and actual objects. Our results revealed clearly that AR and CNR depended on the measurement density asymptotically, having an optimal point for measurement density beyond which more overlapping measurements would not significantly improve the quality of reconstructed images. Optimization of probe geometry based on our method demonstrated that a practical compromise can be attained between DOI spatial resolution and measurement density.
机译：在基于漫射光学成像的大脑激活研究中，光电二极管的几何形状在实现良好的空间分辨率和检测空间均匀性方面都起着重要作用。使用实验室组织模型模型研究并比较了六个光电二极管几何形状的重建图像的质量，该模型在两个单独的位置包含一个嵌入的对象。对于所有六个几何形状，量化每个像素的重叠测量的数量（即，测量密度）及其空间分布，并将其与所得重建图像的质量相关联。后者由重建对象与实际对象之间的面积比（AR）和对比度噪声比（CNR）表示。我们的结果清楚地表明，AR和CNR渐近地取决于测量密度，具有测量密度的最佳点，超过该点，更多重叠的测量将不会显着提高重建图像的质量。基于我们方法的探头几何形状优化表明，可以在DOI空间分辨率和测量密度之间取得实际的折衷。

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