Image restoration for ring-array photoacoustic tomography based on an attention mechanism driven conditional generative adversarial network
Year: 2025
Type: article
Abstract: Ring-Array photoacoustic tomography (PAT) systems have shown great promise in non-invasive biomedical imaging. However, images produced by these systems often suffer from quality degradation due to non-ideal imaging conditions, with common issues including blurring and streak artifacts. To address these challenges, we propose an image restoration method based on a conditional generative adversarial network (CGAN) framework. Our approach integrates a hybrid spatial and channel attention mechanism within a Residual Shifted Window Transformer Module (RSTM) to enhance the generator's performance. Additionally, we have developed a comprehensive loss function to balance pixel-level accuracy, detail preservation, and perceptual quality. We further incorporate a gamma correction module to enhance the contrast of the network's output. Experimental results on both simulated and in vivo data demonstrate that our method significantly improves resolution and restores overall image quality.
Source: Photoacoustics
Authors: Wende Dong, Yanli Zhang, Luqi Hu, Songde Liu, Chao Tian
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,200
Funders: National Natural Science Foundation of China, University of Science and Technology of China, Anhui Provincial Department of Science and Technology, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, Natural Science Foundation of Anhui Province, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
OpenAlex ID: W4409204668