融合结构先验信息的稳态扩散光学断层成像重建算法研究

扩散光学断层成像作为一种无辐射损伤、低成本的光学在体成像技术,有着良好的应用前景,但具有空间分辨率低、难以定量的缺陷.为了提高扩散光学断层成像的分辨率,实现光学参数分布的精确重建,基于有限元方法,提出了融合结构先验信息的稳态扩散光学断层成像重建算法.该算法以扩散近似作为成像模型,通过软先验的Laplace 正则化方法引入由MicroCT提供的空间结构信息.采用伴随法计算Jacobian矩阵,Levenberg-Marquardt方法用来进行迭代优化.仿真结果表明该算法不仅能获得精确的光学参数值分布,而且显著地提高了迭代收敛的速度.

Image reconstruction algorithm for steady-state diffuse optical tomography with structural priori information

Diffuse optical tomography is a non-invasive and non-ionizing optical imaging technique with low cost, while it suffers from low spatial resolution and is very difficult to achieve quantitative measurement. In order to improve the resolution and reconstruct the optical coefficients accurately, in this paper, we present an image reconstruction algorithm based on finite element method for steady-state diffuse tomography with structural priori information. Imaging model is characterized by the steady-state diffuse equation. The spatial structural information from micro-CT is introduced into the inverse problem by the Laplace regularization and Levenberg-Marquardt method to solve the inverse problem where the Jacobian matrix is obtained by adjoint method. The simulation results show that the algorithm presented is able to obtain the accurate distribution of optical coefficients and increase the convergence speed of iteration evidently.