End-To-End Bioluminescence Tomography Reconstruction Based On Convolution Neural Network Scheme.

Abstract

Bioluminescence tomography (BLT) has received a lot of attention as an important technique in bio-optical imaging. Compared with traditional methods, neural network methods have the advantages of fast reconstruction speed and support for batch processing. In this paper, we propose a end-to-end BLT reconstruction based on convolution neural networks scheme. First, 3000 datasets with single source and dual sources projection were conducted by Monte Carlo method, respectively. And three convolution neural networks (VGGNet, ResNet, and DenseNet) were adopted to feature extraction. Then, the filtered features were used as input to the multi-layer perceptron (MLP) to predict the source location. The results of numerical simulation and simulation experiments show, compared with traditional methods, the advantages of our method are including high reconstruction accuracy, faster reconstruction, few parameters, simple reconstruction process and support for batch processing.