RESUMO
Photoacoustic (PA) tomography is a relatively new medical imaging technique that combines traditional ultrasound imaging and optical imaging, which has great application prospects in recent years. To reveal the light absorption coefficient of biological tissues, the images are reconstructed from PA signals by reconstruction algorithms. However, traditional model-based reconstruction method requires a huge number of iterations to obtain relatively good experimental results, which is quite time-consuming. In this paper, we propose to use deep learning method to replace brute parameter adjustment in model-based reconstruction, and speed up the rate of convergence by building convolutional neural networks (CNN). The parameters we defined in our model can be learned automatically. Meanwhile, our method can optimize the increment of gradient in each step of iteration. The numerical experiment validates our method, showing that only three iterations are needed to obtain the satisfactory image quality, which normally requires 10 iterations for tradition method. It demonstrated that efficiency of photoacoustic reconstruction can be greatly improved by our proposed method, compared with traditional model-based methods.
