HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network

TL;DR

This paper introduces HSD-PAM, a dual-branch fusion network that significantly enhances photoacoustic microscopy by achieving high speed, super resolution, and deep penetration simultaneously through hardware-software co-design and deep learning.

Contribution

The paper presents a novel dual-branch fusion network for PAM that improves resolution and speed without sacrificing penetration depth, addressing a key limitation in current systems.

Findings

Imaging speed increased 16 times

Lateral resolution improved 5 times

Deep penetration capability preserved

Abstract

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.