Use of penalty terms in gradient-based iterative reconstruction schemes for optical tomography
Use of penalty terms in gradient-based iterative reconstruction schemes for optical tomography
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DOI：
10.1117/1.1352753
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发表时间：
2001-04-01
影响因子：
3.5
通讯作者：
Bartel, S
中科院分区：
文献类型：
--
作者：
Hielscher, AH;Bartel, S
众所周知，光学层析成像中的重建问题是不适定的。换句话说，介质内部的许多不同的光学属性的空间分布可以导致在所考虑的介质表面上的相同的探测器读数。因此，选择合适的方法来克服这一问题对于任何成功的光学层析图像重建算法都是至关重要的。在这项工作中，我们在一个基于梯度的迭代图像重建方案中解决了这个问题。图像重建被认为是适当定义的目标函数的最小化。目标函数可以被分成比较预测和实际探测器读数的最小平方误差项和可能包含关于系统的先验信息的附加惩罚项。为了有效地最小化该目标函数，计算关于光学特性的空间分布的梯度。除了介绍克服光学层析成像病态的方法中的基本概念外，我们还将展示数值结果，展示以惩罚项表示的先验知识如何改善重建结果。(C)2001年，光学仪器工程师协会。
It is well known that the reconstruction problem in optical tomography is ill-posed. In other words, many different spatial distributions of optical properties inside the medium can lead to the same detector readings on the surface of the medium under consideration. Therefore, the choice of an appropriate method to overcome this problem is of crucial importance for any successful optical tomographic image reconstruction algorithm. In this work we approach the problem within a gradient-based iterative image reconstruction scheme. The image reconstruction is considered to be a minimization of an appropriately defined objective function. The objective function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that may contain a priori information about the system. For the efficient minimization of this objective function the gradient with respect to the spatial distribution of optical properties is calculated. Besides presenting the underlying concepts in our approach to overcome ill-posedness in optical tomography, we will show numerical results that demonstrate how prior knowledge, represented as penalty terms, can improve the reconstruction results. (C) 2001 society of Photo-Optical Instrumentation Engineers.