High resolution optoacoustic reconstruction based on unsupervised learning

Optoacoustic tomography (OAT) is a new biomedical imaging technology, which plays an important role in medical research and clinical practice. Considering the problems of low resolution in optoacoustic tomography, a new high-resolution reconstruction network (Phys-AU-Net) combining physical point spread function (PSF) and convolutional neural network (CNN) is proposed in this paper. Briefly, the proposed method adopts an unsupervised learning strategy, and combines a point spread function (PSF) model and the U-Net based on the attention mechanism. Among these, the PSF model is used to simulate the diffraction limited mechanism, and the U-Net network based on attention mechanism is used to complete the feature extraction of high-density images. The experimental results show that compared with U-Net, the Phys-AU-Net improves the structural similarity (SSIM) by 43.5% and the peak signal to noise ratio (PSNR) by 11.2%, which provides a great potential in clinical research and diagnosis.