Reconstruct the Photoacoustic Image Based On Deep Learning with Multi-frequency Ring-shape Transducer Array

Photoacoustic tomography (PAT) combines the superiorities of both optical imaging and ultrasound imaging, which provides rich optical absorption contrast with 3D spatial information by applying reconstruction algorithms. Classical reconstruction algorithms, e.g. delay-and-sum, have been widely used in photoacoustic imaging. Recently, the deep neural networks have showed the potential to be used to reconstruct the PA images from raw photoacoustic data. In this paper, a framework of the neural network is proposed to approach the PA imaging reconstruction using multi-frequency ultrasound sensor data. Specifically, we trained an end-to-end network to compare the performance when the transducers surround the region of interest with three different center frequencies, which receive PA signals containing different frequency spectrum information from the target. In particular, we trained and tested the network using the factitious segmented vessels' PA images from fundus oculi CT imaging after converting to PA data. From the results of the numerical simulations, the proposed frameworks have shown much better performance compared with conventional reconstruction algorithms. Moreover, the time consumption of the proposed reconstruction method outperforms other conventional reconstruction algorithms, which enables its potential to apply in real-time imaging.