一种基于模型的光学层析图像重建方法

光学层析图像重建是个病态问题,测量误差会在重建过程中被放大,对此,提出一种以广义高斯马尔可夫随机场模型为先验信息的光学层析图像重建方法.重建过程是对目标函数的优化过程,目标函数关于光学参数的梯度计算是算法中的难点,因此,提出一种基于梯度树的梯度计算方法.文中分别给出了吸收系数和散射系数的重建结果,并引入三个指标因子衡量重建图像的质量,进而列出不同重建算法下,重建图像的指标值.最后通过对重建结果和指标因子取值的比较,分析基于模型的重建算法的有效性.

Model-based image reconstruction algorithm for optical tomography

Optical tomography reconstruction is an ill-posed problem and the errors in the measurement data will be amplified in the process of reconstruction.In order to solve the problem of ill-posedness,a new kind of reconstruction algorithm was proposed in this paper,in which the Generalized Gaussian Markov Random Field(GGMRF) was used to model the unknown image.The reconstruction process was an optimization process on objective function in essence,but the gradient computation of the objective function with respect to optical parameters was difficult.Therefore,a novel gradient computation strategy based on gradient tree was presented.In experiments,reconstructed results corresponding to the absorption coefficients and the scattering coefficients were given.In order to evaluate the quality of reconstructed images,three criteria were imported,and their values for images from different reconstruction algorithms were listed.Finally,the validity of the model-based reconstruction algorithm was analyzed by the comparison of reconstructed images and the values of three criteria.