Optimization method of diffuse optical tomography reconstruction based on neural network

摘要

Diffuse Optical Tomography (DOT) is a promising non-invasive optical imaging technology that can provide structural and functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the reverse problem of DOT is ill-posed and ill-conditioned. In order to overcome these limitations, two types of neural networks, back-propagation neural network (BPNN) and stacked autoencoder (SAE) were applied in DOT image reconstruction, which use the internal optical properties distribution and the boundary measurement of biological tissues as the input and output data sets respectively to adjust the neural network parameters, and directly establish a nonlinear mapping of the input and output. To verify the effectiveness of the methods, a series of numerical simulation experiments were conducted, and the experimental results were quantitatively assessed, which demonstrated that both methods can accurately predict the position and size of the inclusion, especially in the case of higher absorption contrast. As a whole, SAE can get better reconstructed image results than BPNN and the training time was only a quarter of BPNN.