Single-shot dual-scale photoacoustic microscopy enables ultra-sparse sampling reconstruction of optical-resolution imaging.
- 1School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, China.
- 2Department of Neurosurgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
Abstract
Optical-resolution photoacoustic microscopy (OR-PAM) enables label-free, subcellular-resolution microvascular imaging, but the speed-resolution trade-off remains a bottleneck. While sparse sampling accelerates acquisition, the disparity between OR-PAM's tight optical focus and enlarged scanning steps causes irreversible information loss. To mitigate the limitations of ultra-sparse sampling, a single-shot dual-scale PAM framework is proposed. An independent fiber-based optical foci-steering configuration enables synchronized OR and acoustic-resolution (AR) acquisition, which ensures scanning flexibility while harnessing AR signals to enhance reconstruction. The AR modality, governed by its broader acoustic point-spread function (PSF), acts as an intrinsic physical low-pass filter that robustly captures the target's dominant vascular architecture. Guided by the theoretical AR-PSF, a deblurring operation is performed to obtain a sharpened structural prior. Subsequently, a dual-scale super-resolution generative adversarial network fuses this prior with ultra-sparse OR measurements to reconstruct high-fidelity 2D vascular MAP images. For reconstruction evaluation, the ultra-sparse inputs were generated by retrospectively decimating densely sampled in vivo data. Experimental results demonstrate that the proposed method significantly outperforms representative leading methods. By overcoming extreme spatial information scarcity under high-ratio sparse sampling up to 16 × 16 (0.4% sampling rate), the proposed method achieves a 16.1% SSIM improvement and a 2.18 dB PSNR gain over the best-performing baseline, together with a 33.7% reduction in LPIPS.
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BibTeX
@article{chen2026singleshot,
title = {{Single-shot dual-scale photoacoustic microscopy enables ultra-sparse sampling reconstruction of optical-resolution imaging.}},
author = {Kexin Chen and Zhouyan Cao and Ruiqin Li and Haiping Cai and Xiaobin Hong and Jiangbo Chen},
journal = {Photoacoustics},
year = {2026},
month = oct,
doi = {10.1016/j.pacs.2026.100895},
url = {https://europepmc.org/article/MED/42927315},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Single-shot dual-scale photoacoustic microscopy enables ultra-sparse sampling reconstruction of optical-resolution imaging.
AU - Kexin Chen
AU - Zhouyan Cao
AU - Ruiqin Li
AU - Haiping Cai
AU - Xiaobin Hong
AU - Jiangbo Chen
PY - 2026
DA - 2026/10/07
JO - Photoacoustics
DO - 10.1016/j.pacs.2026.100895
UR - https://europepmc.org/article/MED/42927315
AB - Optical-resolution photoacoustic microscopy (OR-PAM) enables label-free, subcellular-resolution microvascular imaging, but the speed-resolution trade-off remains a bottleneck. While sparse sampling accelerates acquisition, the disparity between OR-PAM's tight optical focus and enlarged scanning steps causes irreversible information loss. To mitigate the limitations of ultra-sparse sampling, a single-shot dual-scale PAM framework is proposed. An independent fiber-based optical foci-steering configuration enables synchronized OR and acoustic-resolution (AR) acquisition, which ensures scanning flexibility while harnessing AR signals to enhance reconstruction. The AR modality, governed by its broader acoustic point-spread function (PSF), acts as an intrinsic physical low-pass filter that robustly captures the target's dominant vascular architecture. Guided by the theoretical AR-PSF, a deblurring operation is performed to obtain a sharpened structural prior. Subsequently, a dual-scale super-resolution generative adversarial network fuses this prior with ultra-sparse OR measurements to reconstruct high-fidelity 2D vascular MAP images. For reconstruction evaluation, the ultra-sparse inputs were generated by retrospectively decimating densely sampled in vivo data. Experimental results demonstrate that the proposed method significantly outperforms representative leading methods. By overcoming extreme spatial information scarcity under high-ratio sparse sampling up to 16 × 16 (0.4% sampling rate), the proposed method achieves a 16.1% SSIM improvement and a 2.18 dB PSNR gain over the best-performing baseline, together with a 33.7% reduction in LPIPS.
SN - 2213-5979
ER -