Kavuri Venkaiah C, Lin Zi-Jing, Tian Fenghua, Liu Hanli
Biomed Opt Express. 2012 May 1;3(5):943-57. doi: 10.1364/BOE.3.000943. Epub 2012 Apr 12.
In diffuse optical tomography (DOT), researchers often face challenges to accurately recover the depth and size of the reconstructed objects. Recent development of the Depth Compensation Algorithm (DCA) solves the depth localization problem, but the reconstructed images commonly exhibit over-smoothed boundaries, leading to fuzzy images with low spatial resolution. While conventional DOT solves a linear inverse model by minimizing least squares errors using L2 norm regularization, L1 regularization promotes sparse solutions. The latter may be used to reduce the over-smoothing effect on reconstructed images. In this study, we combined DCA with L1 regularization, and also with L2 regularization, to examine which combined approach provided us with an improved spatial resolution and depth localization for DOT. Laboratory tissue phantoms were utilized for the measurement with a fiber-based and a camera-based DOT imaging system. The results from both systems showed that L1 regularization clearly outperformed L2 regularization in both spatial resolution and depth localization of DOT. An example of functional brain imaging taken from human in vivo measurements was further obtained to support the conclusion of the study.
在漫射光学层析成像（DOT）中，研究人员常常面临准确恢复重建物体的深度和尺寸的挑战。深度补偿算法（DCA）的最新发展解决了深度定位问题，但重建图像通常呈现过度平滑的边界，导致空间分辨率低的模糊图像。传统的DOT通过使用L2范数正则化最小化最小二乘误差来求解线性逆模型，而L1正则化促进稀疏解。后者可用于减少对重建图像的过度平滑效应。在本研究中，我们将DCA与L1正则化以及L2正则化相结合，以研究哪种组合方法能为DOT提供更高的空间分辨率和深度定位。使用基于光纤和基于相机的DOT成像系统对实验室组织模型进行测量。两个系统的结果均表明，在DOT的空间分辨率和深度定位方面，L1正则化明显优于L2正则化。进一步获取了一个来自人体活体测量的功能性脑成像示例来支持该研究的结论。
