  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, as three involved critical imaging parameters,
imaging speed, lateral resolution, and penetration depth 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. Here, 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 the deep penetration capability, we aim to enhance the
lateral resolution and up sampling the images, so that high speed, super
resolution, and deep penetration for the PAM system (HSD-PAM) can be achieved.
Data-driven based algorithm is a promising approach to solve this issue,
thereby a dedicated novel dual branch fusion network is proposed, which
includes a high resolution branch and a high speed branch. Since the
availability of switchable AR-OR-PAM imaging system, the corresponding low
resolution, undersample AR-PAM and high resolution, full sampled OR-PAM image
pairs are utilized for training the network. Extensive simulation and in vivo
experiments have been conducted to validate the trained model, enhancement
results have proved the proposed algorithm achieved the best perceptual and
quantitative image quality. 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.
