摘要
Photoacoustic microscopy (PAM) is a novel implementation of photoacoustic imaging (PAI) for visualizing the 3D bio-structure, which is realized by raster scanning of the tissue. However, temporal resolution, lateral resolution, and penetration depth, as three involved critical imaging parameters, have mutual effect to one the other. The improvement of one parameter results in the degradation of other two parameters, which constrains the overall performance of the PAM system. In this work, we propose to break these limitations by hardware and software co-design. Starting with low lateral resolution, low sampling rate AR-PAM imaging which possesses deep penetration capability, we aim to enhance the lateral resolution and upscale the images, so that high temporal resolution (scanning speed), high lateral resolution, and deep penetration for the PAM system can be achieved. Considering huge information gap between input image and target image, a dedicated dual branch network is proposed, which includes a high resolution branch and a high speed branch to fully extract embedded information from training data. What's more, to effectively fuse the high level semantic information and low level spatial details from these two branches, a novel self-attention and graph induced fusion module is designed. This module significantly eliminates blurring effect to enhance imaging resolution and amplifies the true signal to increase imaging contrast, as proved by comparison algorithms. As a result, the imaging speed is increased 16× times and the imaging lateral resolution is improved 5× times, while the deep penetration merit of AR-PAM modality is still reserved.