OGRR-Net: observation-guided residual refinement learning for photoacoustic imaging
Year: 2026
Type: article
Abstract: Photoacoustic imaging provides optical-absorption contrast through ultrasonic detection. However, image reconstruction remains affected by acquisition geometry, detector response, acoustic noise, and the attenuation of high-frequency components during analytic inversion. Although deep post-processing methods have shown effectiveness in artifact reduction, purely image-domain restoration lacks a direct mechanism for incorporating complementary information contained in the raw measurement data. This work proposes OGRR-Net, an observation-guided residual refinement network for photoacoustic imaging. The network takes an initial reconstruction as the spatial reference and encodes the corresponding raw sinogram into an observation-guidance map, which conditions a gated residual correction pathway for image refinement. Evaluated on an in vivo mouse dataset and the OADAT optoacoustic dataset against representative image-domain and hybrid reconstruction methods, the proposed observation-guided strategy improves reconstruction fidelity, reduces artifacts, and enhances the preservation of structural details.
Source: Optics Express
DOI: 10.1364/oe.611535
Authors: Qi Liu, Fengchen Luo, Hengrong Lan, Fei Gao, Siyu Liu
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,270
OpenAlex ID: W7222921064