Real-time deep learning-based vascular recognition in clinical breast photoacoustic imaging.

Wenhan Zheng, Zeyang Sun, Keer Huang, Li Lin, Zhenzhong Liu, Xiaoxiao Xu, Yunrui Cao, Qingjun Liu

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Article in Photoacoustics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Wenhan ZhengTaizhou Key Laboratory of Medical Devices and Advanced Materials, Taizhou Institute of Zhejiang University, Taizhou, 318000, China.

Zeyang SunCollege of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China.

Keer HuangCollege of Biomedical Engineering and Instrument Science, Zhejiang University, 310027, Hangzhou, China.

Li LinCollege of Biomedical Engineering and Instrument Science, Zhejiang University, 310027, Hangzhou, China.

Zhenzhong LiuTaizhou Key Laboratory of Medical Devices and Advanced Materials, Taizhou Institute of Zhejiang University, Taizhou, 318000, China.

Xiaoxiao XuCollege of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, 330004, China.

Yunrui CaoCollege of Biomedical Engineering and Instrument Science, Zhejiang University, 310027, Hangzhou, China.

Qingjun LiuTaizhou Key Laboratory of Medical Devices and Advanced Materials, Taizhou Institute of Zhejiang University, Taizhou, 318000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast imaging is a primary application of photoacoustic computed tomography. However, in current clinical environments, existing technologies cannot perform real-time vascular recognition during the scanning process. We propose a deep learning-based vascular recognition method. A physics-informed simulation workflow was constructed to train the network, alleviating the challenge of scarce high-quality clinical data. By optimizing the proposed HAR-UNet (Hybrid Attention Residual U-Net), an end-to-end processing speed of 18.08 fps was achieved. This high-frame-rate output enables real-time clinical vascular recognition. Furthermore, the clinical translational potential of this method was evaluated on an in vivo cohort of breast cancer patients. The proposed framework enables synchronous clinical vascular scanning and recognition. Quantitative analysis performed on 23 valid cases effectively assessed tumor angiogenesis biomarkers, differentiating tumors from normal tissues with a

Indexed as

Deep learningPhotoacoustic imagingReal-time inferenceVascular recognition

Identifiers

PMID42603879

DOI10.1016/j.pacs.2026.100863

PMCPMC13476369

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.