Mr. Xiongjun Cao Profile

Xiongjun Cao

at Nanchang Univ.

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Author

Publications (9)

Proceedings Article | 29 March 2023 Open Access

A novel volumetric fusion algorithm for optical-resolution photoacoustic microscopy based on 3D-SWT and joint weighted evaluation optimization

Xianlin Song, Sihang Li, Zhuangzhuang Wang, Xiongjun Cao

Proceedings Volume 12601, 1260104 (2023) https://doi.org/10.1117/12.2666551

KEYWORDS: Photoacoustic spectroscopy, Image fusion, Photoacoustic microscopy, Data fusion, Wavelets, 3D image processing, Stationary wavelet transform, Mathematical optimization, Photoacoustic imaging, Depth of field

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Proceedings Article | 14 November 2021 Presentation + Paper

Three-dimensional high-resolution information fusion method for optical-resolution photoacoustic microscopy using principal component analysis transform

Xiongjun Cao, Zhihui Li, Xianlin Song

Proceedings Volume 11914, 119141H (2021) https://doi.org/10.1117/12.2612462

KEYWORDS: Principal component analysis, Photoacoustic spectroscopy, 3D photoacoustic microscopy, Photoacoustic microscopy, Image fusion, Information fusion, Image resolution, 3D image processing, Photoacoustic imaging, Data fusion

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Proceedings Article | 14 September 2021 Presentation + Paper

3D information fusion of optical-resolution photoacoustic microscopy for extended depth of field using pyramid transform

Xiongjun Cao, Zhihui Li, Xianlin Song

Proceedings Volume 11875, 118750H (2021) https://doi.org/10.1117/12.2601652

KEYWORDS: Photoacoustic microscopy, Image fusion, 3D photoacoustic microscopy, Photoacoustic imaging, Image resolution, Information fusion, Data fusion, 3D image processing, Focus stacking, Optical imaging

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Proceedings Article | 12 September 2021 Presentation + Paper

High-resolution 3D information fusion for photoacoustic microscopy using Laplace pyramid transform

Zhihui Li, Xiongjun Cao, Xianlin Song

Proceedings Volume 11871, 1187110 (2021) https://doi.org/10.1117/12.2596676

KEYWORDS: Photoacoustic microscopy, Image fusion, Information fusion, 3D photoacoustic microscopy, Photoacoustic imaging, Photoacoustic spectroscopy, Image processing, Data fusion, 3D image processing, Medical imaging

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Proceedings Article | 31 August 2021 Paper

Optimal design of bifocal acoustic lens for large volumetric photoacoustic detection using ZEMAX

Huiyan Jiang, Yuwei Zhu, ZouHua Chen, Xiongjun Cao, Xianlin Song

Proceedings Volume 11907, 119073C (2021) https://doi.org/10.1117/12.2603841

KEYWORDS: Acoustics, Calibration, Imaging systems, Modulation transfer functions, Zemax, Photoacoustic spectroscopy, Image quality, Photoacoustic imaging, Optimization (mathematics), Lens design

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Proceedings Article | 31 August 2021 Paper

Three-dimensional fusion for large volumetric optical-resolution photoacoustic microscopy

Xiongjun Cao, Zhihui Li, Xianlin Song

Proceedings Volume 11907, 1190738 (2021) https://doi.org/10.1117/12.2603388

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Proceedings Article | 1 August 2021 Poster + Paper

Large volumetric optical-resolution photoacoustic microscopy with image fusion based on CNN feature extraction

Sihang Li, Zhuangzhuang Wang, Xiongjun Cao, Zhihui Li, Xianlin Song

Proceedings Volume 11814, 118140J (2021) https://doi.org/10.1117/12.2592123

KEYWORDS: Image fusion, Photoacoustic microscopy, Feature extraction, Photoacoustic imaging, Photoacoustic spectroscopy, 3D image processing, Image resolution, Convolutional neural networks, Acoustics, 3D photoacoustic microscopy

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Proceedings Article | 1 August 2021 Presentation + Paper

Large depth of field high-resolution 3D information fusion for optical-resolution photoacoustic microscopy using wavelet transform

Xiongjun Cao, Zhihui Li, Xianlin Song

Proceedings Volume 11841, 118410F (2021) https://doi.org/10.1117/12.2592084

KEYWORDS: Image fusion, Photoacoustic microscopy, Wavelet transforms, Photoacoustic imaging, 3D photoacoustic microscopy, Wavelets, Photoacoustic spectroscopy, Information fusion, Tissues, 3D image processing

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Proceedings Article | 20 June 2021 Presentation + Paper

High-speed and flexible 3D high-resolution fusion of photoacoustic information based on visualization software

Zhihui Li, Xiongjun Cao, Xianlin Song

Proceedings Volume 11788, 117880H (2021) https://doi.org/10.1117/12.2592170

KEYWORDS: Signal detection, Photoacoustic spectroscopy, Microrings, Tissues, Resonators, Ultrasonics, Visualization, Finite element methods, Photoacoustic imaging, Brain

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Currently, there are four major medical imaging technologies in the clinic: X-ray computer tomography (X-CT), ultrasound scan imaging (US), positron emission tomography (PET), and magnetic resonance imaging (MRI). They have developed rapidly, but they all have their own disadvantages. Compared with the above four imaging methods, the photoacoustic imaging (PAI) technology based on the photoacoustic effect has been extensively researched. PAI combines the high-contrast advantages of pure optical imaging and the high resolution of pure ultrasound imaging, which can achieve deeper imaging of biological tissues, and is currently widely used in biological imaging. The research in this paper is the expansion of the depth of field of the PAI system. The basic principle of PAI is to irradiate the target tissue with a short pulse laser, to excite ultrasound. The light absorption distribution reflecting the internal structure of the target tissue is reconstructed from the sampled sound waves. Since it needs to focus the laser strongly, this will result in a small imaging depth of field. Many biological tissues (such as brain, abdomen, etc.) have curved surfaces. Due to the limited depth of field of the optical imaging system, the blood vessels in the imaging field of view may not be on the same focal plane. It is arduous to get all accurately focused blood vessel images. It is not conducive to the observation of changes in the structure and composition of blood vessels, which brings great inconvenience to related researchers. In order to solve this problem, this paper proposes a large depth of field and high-resolution three-dimensional photoacoustic information fusion technology suitable for PAI systems. In this paper, the virtual blood vessel data sets are used to simulate the three-dimensional data sets of blood vessels at different focal positions collected by the PAI system. Based on the visualization software Amira, the two sets of three-dimensional data are reconstructed and fused. The results demonstrate that the fused virtual blood vessel imaging structure shows a complete high-resolution structure in the entire field of view. The validity and reliability of the method are verified.

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