Enhancement of the depth of field of photoacoustic microscopy based on deep learning

Gang Hu; Yanan Hu; Rui Wang; Xiaohai Yu; Xianlin Song

doi:10.1117/12.2683129

18 July 2023 Enhancement of the depth of field of photoacoustic microscopy based on deep learning

Gang Hu, Yanan Hu, Rui Wang, Xiaohai Yu, Xianlin Song

Proceedings Volume 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023); 127450E (2023) https://doi.org/10.1117/12.2683129
Event: Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 2023, Haikou, China

Abstract

As a noninvasive biomedical imaging modality, photoacoustic imaging has shown great potential for clinical application recently with simultaneous high contrast and penetration depth. Optical-resolution photoacoustic microscopy is one important branch of photoacoustic imaging, which can achieve high spital resolution. However, this technique suffers from limited depth of field due to the strongly focused Gaussian beam used. With the development of deep learning in various medical imaging techniques, deep learning also has had a significant impact on the field of photoacoustic imaging in recent years. This paper presents a novel method to improve the depth of field of photoacoustic microscope by integrating the U-net semantic segmentation model with the simulation platform of photoacoustic microscopy based on k-Wave. First, we imaged the blood vessels on the simulation platform to obtain the vascular slice B-scan images and the corresponding ground truth images, and then the dataset was randomly divided into the training dataset and the testing dataset in a ratio of 7:1. During the U-Net model training process, the B-scan images serve as the input to the model while corresponding ground truth images serve as the labels. Finally, this study demonstrates the potential of the U-Net model to improve the depth of field of photoacoustic imaging. And this method effectively improves the accuracy of obtaining structural features of tissues, which has significant implications for the diagnosis and treatment of diseases.

Citation Download Citation

Gang Hu, Yanan Hu, Rui Wang, Xiaohai Yu, and Xianlin Song "Enhancement of the depth of field of photoacoustic microscopy based on deep learning", Proc. SPIE 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 127450E (18 July 2023); https://doi.org/10.1117/12.2683129

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KEYWORDS

Education and training

Depth of field

Data modeling

Photoacoustic microscopy

Deep learning

Performance modeling

Photoacoustic imaging

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