Photoacoustic microscopy with sparse data by convolutional neural networks

Jiasheng Zhou; Da He; Xiaoyu Shang; Zhendong Guo; Sung-Liang Chen; Jiajia Luo

doi:10.1016/j.pacs.2021.100242

Photoacoustic microscopy with sparse data by convolutional neural networks

Photoacoustics. 2021 Feb 2:22:100242. doi: 10.1016/j.pacs.2021.100242. eCollection 2021 Jun.

Authors

Jiasheng Zhou¹, Da He¹, Xiaoyu Shang¹, Zhendong Guo¹, Sung-Liang Chen¹, Jiajia Luo²

Affiliations

¹ University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
² Biomedical Engineering Department, Peking University, Beijing 100191, China.

Abstract

The point-by-point scanning mechanism of photoacoustic microscopy (PAM) results in low-speed imaging, limiting the application of PAM. In this work, we propose a method to improve the quality of sparse PAM images using convolutional neural networks (CNNs), thereby speeding up image acquisition while maintaining good image quality. The CNN model utilizes attention modules, residual blocks, and perceptual losses to reconstruct the sparse PAM image, which is a mapping from a 1/4 or 1/16 low-sampling sparse PAM image to a latent fully-sampled one. The model is trained and validated mainly on PAM images of leaf veins, showing effective improvements quantitatively and qualitatively. Our model is also tested using in vivo PAM images of blood vessels of mouse ears and eyes. The results suggest that the model can enhance the quality of the sparse PAM image of blood vessels in several aspects, which facilitates fast PAM and its clinical applications.

Keywords: Convolutional neural network; Image enhancement; Photoacoustic microscopy; Sparse image.