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

Authors

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

Published in

Photoacoustics. Volume 51. Pages 100863. Epub Aug 03, 2026.

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 $P$ -value of less than 0.0001. These results suggest that this method can provide real-time imaging assistance for the clinical evaluation and diagnosis of breast cancer.

PMID:
42603879
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.