Optical tomography aims to reconstruct the cross-sectional distribution from numerous projections along various orientations. Due to its 'hard-field', high spatial and temporal resolution, this technique has been widely used in multi-phase flow monitoring, temperature and species concentration measurement and functional tissue imaging. Optical tomography adopts light emitters to emit laser beams, which are attenuated by the medium. The outgoing light is then detected by photosensitive receivers. Reconstruction algorithms are used to reconstruct the absorption distribution of the medium. Intrusively, increasing light beams and receivers will improve the reconstruction performance. However, this approach is not appropriate when the light access or installation space is limited. Meanwhile, the reported tomography sensors usually have regular arrangement, which forms a non-uniform sensitivity matrix and the region of interest (ROI) is detected unevenly. In this work, we propose an optimization method based on uniformity coefficient and genetic algorithm (GA). We hope our method can provide an optimized sensor configuration that has a uniform sensitivity matrix and improved reconstruction performance.
Sensitivity matrix relates the practical distribution to the numerous projections, which is important for image reconstruction. It is well recognized that uniform sensitivity matrix promises improved reconstruction performance. While the reconstructed images have large error when the matrix has low uniformity. In this work, uniformity coefficient is introduced to represent the uniformity of the matrix. Meanwhile, we assume that the uniformity coefficient is directly related to the quality of image reconstruction, namely, lower uniformity coefficient leads to improved reconstruction performance, and larger value leads to deteriorated performance. The optimization procedure mainly includes the following steps. Firstly, reconstruction with 60 configurations and 10 distributions are implemented to verify the effectiveness of the uniformity coefficient as a predictor. The number of the light emitters and receivers are both 25. Secondly, we adopt GA to optimize the arrangement of the emitters and receivers. The fitness function is set as the uniformity coefficient. Finally, we analyze the optimized configuration and compare its reconstruction performance with the random and regular configurations.
This paper presents an optimization method for optical tomography sensor configuration based on GA. The following conclusions can be concluded. Firstly, simulation experiments of randomly generated configurations and distributions verify that the uniformity coefficient is an effective predictor for reconstruction performance. Configuration with low uniformity coefficient has uniform sensitivity matrix and beam arrangement, and improved reconstruction performance. On the contrary, configuration with large uniformity coefficient has uneven beam arrangement, and its reconstruction performance is deteriorated. Secondly, GA is used to implement the optimization, and we take the uniformity coefficient as the fitness function. The optimized configuration provided by GA has a uniformity coefficient of 0.288. Different distributions have been considered and the reconstruction results indicate the superiority of the optimized configuration over the random and regular configurations. The optimization method has been proven to be effective. Thirdly, reconstruction results display that the practical distribution has significant influence on the performance of the configurations. Since the uniformity coefficient is only related to the configuration, the optimization results are independent to the practical distribution and this optimization method can be used in the applications where the priori information of the distributions is difficult to obtain.