Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography.
Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography.
复制标题
DOI：
10.1364/boe.3.000943
复制
发表时间：
2012-05-01
影响因子：
3.4
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Kavuri VC;Lin ZJ;Tian F;Liu H
摘要：在漫反射光学断层扫描（DOT）中，研究人员经常面临准确恢复重建对象的深度和尺寸的挑战。最近发展的深度补偿算法（DCA）解决了深度定位问题，但重建图像通常表现出过度平滑的边界，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 正则化则促进稀疏解。后者可用于减少对重建图像的过度平滑影响。在本研究中，我们将 DCA 与 L1 正则化以及 L2 正则化相结合，以检查哪种组合方法为我们提供了改进的 DOT 空间分辨率和深度定位。使用实验室组织模型通过基于光纤和基于相机的 DOT 成像系统进行测量。两个系统的结果表明，L1 正则化在 DOT 的空间分辨率和深度定位方面明显优于 L2 正则化。进一步获得了从人体体内测量中获取的功能性脑成像的例子，以支持该研究的结论。
Abstract: 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.