Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network

Jing Meng; Xueting Zhang; Liangjian Liu; Silue Zeng; Chihua Fang; Chengbo Liu

doi:10.1364/BOE.461183

Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network

Biomed Opt Express. 2022 Jul 27;13(8):4386-4397. doi: 10.1364/BOE.461183. eCollection 2022 Aug 1.

Authors

Jing Meng^{1

2}, Xueting Zhang^{1

2}, Liangjian Liu^{3

2}, Silue Zeng^{3

4}, Chihua Fang⁴, Chengbo Liu³

Affiliations

¹ School of Computer, Qufu Normal University, Rizhao 276826, China.
² These authors contributed equally to this work.
³ Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
⁴ Department of Hepatobiliary Surgery I, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.

Abstract

Acoustic resolution photoacoustic microscopy (AR-PAM) is a major modality of photoacoustic imaging. It can non-invasively provide high-resolution morphological and functional information about biological tissues. However, the image quality of AR-PAM degrades rapidly when the targets move far away from the focus. Although some works have been conducted to extend the high-resolution imaging depth of AR-PAM, most of them have a small focal point requirement, which is generally not satisfied in a regular AR-PAM system. Therefore, we propose a two-stage deep learning (DL) reconstruction strategy for AR-PAM to recover high-resolution photoacoustic images at different out-of-focus depths adaptively. The residual U-Net with attention gate was developed to implement the image reconstruction. We carried out phantom and in vivo experiments to optimize the proposed DL network and verify the performance of the proposed reconstruction method. Experimental results demonstrated that our approach extends the depth-of-focus of AR-PAM from 1mm to 3mm under the 4 mJ/cm² light energy used in the imaging system. In addition, the imaging resolution of the region 2 mm far away from the focus can be improved, similar to the in-focus area. The proposed method effectively improves the imaging ability of AR-PAM and thus could be used in various biomedical studies needing deeper depth.