基于并行BP神经网络的近红外光断层图像重建方法基础研究

提出一种基于并行BP神经网络的近红外光断层成像(Near-infrared optical tomography,NIR OT)图像重建算法,利用BP神经网络来表征生物组织内部光学参数的空间分布和边界光强之间的非线性映射关系.该方法将一个复杂的模型分解成简单的模型分别建立并行的神经网络.利用Femlab软件完成基于有限元的稳态扩散方程的两个简单模型的正向问题求解,根据提出的平均优化散射系数和正向问题训练的大量数据集合,建立并训练并行神经网络,通过对两个网络结果的分析,实现快速获得更复杂模型的光学参数的重构.算法能够快速识别特异组织的位置和准确反映热疗过程中生物组织的优化散射系数的变化趋势.

An image reconstruction approach for near-infrared optical tomography based on parallel BP neural network

An image reconstruction approach for near-infrared optical tomography (NIR OT) based on a parallel neural network is presented. The parallel BP neural network is used to distinguish the nonlinear relationship between the spatial location of tumor and light intensity around the boundary of tissue. The method turns a complicated model into two simpler ones to build two parallel BP neural networks. The steady state diffusion equation of the two simple models is solved by Femlab software. The inverse problem is solved as optimization problem by Levenberg-Marquardt algorithm. The concept of the average optical coefficient is proposed, which is helpful to understand the distribution of the scattering photon from tumor. The reconstruction can be obtained by the trained network. The fast reconstruction of tissue optical properties and provided reconstruction of OT with a new method can be realized by proposed algorithm.