Schweiger M, Dorn O, Zacharopoulos A, Nissila I, Arridge S R
Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
Opt Express. 2010 Jan 4;18(1):150-64. doi: 10.1364/OE.18.000150.
The level set technique is an implicit shape-based image reconstruction method that allows the recovery of the location, size and shape of objects of distinct contrast with well-defined boundaries embedded in a medium of homogeneous or moderately varying background parameters. In the case of diffuse optical tomography, level sets can be employed to simultaneously recover inclusions that differ in their absorption or scattering parameters from the background medium. This paper applies the level set method to the three-dimensional reconstruction of objects from simulated model data and from experimental frequency-domain data of light transmission obtained from a cylindrical phantom with tissue-like parameters. The shape and contrast of two inclusions, differing in absorption and diffusion parameters from the background, respectively, are reconstructed simultaneously. We compare the performance of level set recons uction with results from an image-based method using a Gauss-Newton iterative approach, and show that the level set technique can improve the detection and localisation of small, high-contrast targets.
水平集技术是一种基于隐式形状的图像重建方法，它能够在背景参数均匀或适度变化的介质中，恢复具有清晰边界、对比度明显的物体的位置、大小和形状。在扩散光学层析成像中，水平集可用于同时恢复与背景介质在吸收或散射参数上不同的内含物。本文将水平集方法应用于从模拟模型数据以及从具有类组织参数的圆柱形体模获得的光传输实验频域数据中对物体进行三维重建。同时重建了两个分别在吸收和扩散参数上与背景不同的内含物的形状和对比度。我们将水平集重建的性能与使用高斯 - 牛顿迭代法的基于图像的方法的结果进行比较，结果表明水平集技术能够提高对小的、高对比度目标的检测和定位能力。
