摘要
光学层析成像是一个病态重建过程,为降低重建过程中的病态特性,需加入合适的先验信息。目前,大多数重建都是基于扩散方程的,在某些情况下,这种重建会失败。直接基于玻耳兹曼传输模型,并以图像熵为正则化项的梯度迭代重建是一种有效的方法。该方法中,梯度计算是个难点。对此,提出一种基于梯度树的求解方法,降低光学层析图像重建的病态性,有效地重建光学层析图像。
It is well known that optical tomography （OT） is an ill-posed problem and some proper a priori information is incorporated in order to decrease the ill-poseness. At present, most of the reconstructions are based on diffusion equation, which will fail in some cases. Hence, the reconstruction process is put forward based on Boltzmann transport model directly with the image entropy as the regularized item, which is implemented by the gradient-based iterative reconstruction scheme, but the gradient computation of objective function with respect to optical parameters is difficult. So a gradient calculation strategy based on gradient tree is proposed. Experimental results show that OT image is reconstructed effectively, its ill-poseness is decreased, and the reconstruction quality at the same time is improved.
出处
《计算机工程与设计》
CSCD
北大核心
2006年第23期4416-4418,共3页
Computer Engineering and Design
基金
国家自然科学基金项目(60072014)
山东省自然科学基金项目(Y2003G01)
关键词
光学层析成像
图像重建
迎风差分离散坐标方法
联合差分方法
玻耳兹曼传输模型
最大熵
optical tomography
image reconstruction
upwind-difference discrete-ordinates method
adjoint differentiation scheme
Boltzmann transport model
maximum entropy
