Eames Matthew E, Pogue Brian W, Yalavarthy Phaneendra K, Dehghani Hamid
Opt Express. 2007 Nov 26;15(24):15908-19. doi: 10.1364/oe.15.015908.
Model based image reconstruction in Diffuse Optical Tomography relies on both the numerical accuracy of the forward model as well as the computational speed and efficiency of the inverse model. Most model based image reconstruction algorithms rely on Newton type inversion methods, whereby the inverse of a large Jacobian is approximated. In this work we present an efficient Jacobian reduction method which takes into account the total sensitivity of the imaging domain to the measured boundary data. It is shown using numerical and phantom data that by removing regions within the inverse model whose contribution to the measured data is less than 1%, it has no significant effect upon the estimated inverse problem, but does provide up to a 14 fold improvement in computational time.
基于模型的漫射光学层析成像中的图像重建依赖于正向模型的数值精度以及反向模型的计算速度和效率。大多数基于模型的图像重建算法依赖于牛顿型反演方法，即对一个大的雅可比矩阵的逆进行近似。在这项工作中，我们提出了一种有效的雅可比矩阵约简方法，该方法考虑了成像域对测量边界数据的总灵敏度。使用数值数据和体模数据表明，通过去除反向模型中对测量数据贡献小于1%的区域，对估计的反问题没有显著影响，但在计算时间上最多可提高14倍。
