Mr. Hongzhi Zuo Profile

Hongzhi Zuo

at Tsinghua Univ

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Author

Publications (5)

Proceedings Article | 18 December 2023 Paper

passFormer: heterogeneous feature-aware CNN-transformer coupling network for sparse sampling photoacoustic tomography

Liujie Gu, Yize Gao, Hongzhi Zuo, Cheng Ma

Proceedings Volume 12967, 129670H (2023) https://doi.org/10.1117/12.3008548

KEYWORDS: Image restoration, Photoacoustic tomography, Aliasing, In vivo imaging, Feature extraction

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Photoacoustic imaging (PAI) is an emerging modality that has generated increasing interest for its uses in clinical research and translation. To fully exploit its potential for various preclinical and clinical applications, it is necessary to develop systems that offer high imaging speed, reasonable cost, and manageable data flow. Currently, a significant challenge lies in the fabrication of ultrasound arrays, as many of them are not densely populated enough to fully sample the signals. Ideally, the pitch of the arrays should be half of the center ultrasonic wavelength to prevent spatial undersampling and the subsequent reconstruction artifacts, such as aliasing artifacts and structural deformation. Here, a novel photoacoustic sparse sampling Transformer-CNN coupling network (passFormer) is proposed to decouple target details and spatial under-sampling artifacts from high-frequency image information in a heterogeneous feature-aware manner. To be specific, we first decompose the sparse sampling (SS) photoacoustic (PA) images into 2 parts: high-frequency (HF) and low-frequency (LF) compositions. Our methodology incorporates two bridging modules, the LF modules and the HF modules. The LF coupling module extracts content features (X_c) and latent texture feature (X_tex), and the HF coupling module extracts high-frequency embedding (X_emb) containing the target details features (X_detail) and under-sampling artifacts (X_art). We feed X_t and X_emb into a modified transformer with three encoders and decoders to obtain well-refined HF texture features. At last, we combine the refined HF texture features with pre-extracted X_c by pixel-wise summation reconstruction. Experimental results on publicly available full and sparse reconstruction datasets of mouse and phantom PA images highlight the superior performance of our method, particularly in live mouse imaging. This new approach enables accelerated data acquisition and image reconstruction, facilitating the development of practical and cost-effective imaging systems.

SPIE Journal Paper | 1 July 2022 Open Access

Multi-segmented feature coupling for jointly reconstructing initial pressure and speed of sound in photoacoustic computed tomography

Kexin Deng, Xuanhao Wang, Chuangjian Cai, Manxiu Cui, Hongzhi Zuo, Jianwen Luo, Cheng Ma

JBO, Vol. 27, Issue 07, 076001, (July 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.7.076001

KEYWORDS: Photoacoustic tomography, Image quality, Tissues, Signal detection, Ultrasonography, Image processing, Reconstruction algorithms, Image restoration, Animals, Correlation coefficients

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Proceedings Article | 9 October 2021 Poster + Paper

A spectral-domain model-based method for simultaneous oxygen saturation quantification and contrast agent identification

Hongzhi Zuo, Manxiu Cui, Bangyan Wang, Cheng Ma

Proceedings Volume 11900, 1190036 (2021) https://doi.org/10.1117/12.2602598

KEYWORDS: Photoacoustic spectroscopy, Blood oxygen saturation, Tissue optics, Absorption, Scattering, Multispectral imaging, Light scattering

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Proceedings Article | 10 October 2020 Presentation

Photoacoustic computed tomography with adaptive wavefront correction

Manxiu Cui, Hongzhi Zuo, Kexin Deng, Xuanhao Wang, Cheng Ma

Proceedings Volume 11553, 115530M (2020) https://doi.org/10.1117/12.2574945

KEYWORDS: Photoacoustic tomography, Wavefronts, Wavefront distortions, Ultrasonography, Tissues, Adaptive optics, Image quality, Deep tissue imaging, Photoacoustic spectroscopy, Light scattering

Proceedings Article | 10 October 2020 Presentation

A convex cone method for accurate blood oxygenation photoacoustic imaging

Manxiu Cui, Hongzhi Zuo, Bangyan Wang, Xuanhao Wang, Cheng Ma

Proceedings Volume 11550, 1155006 (2020) https://doi.org/10.1117/12.2574835

KEYWORDS: Photoacoustic imaging, Blood, Tissue optics, Blood oxygen saturation, In vivo imaging, Data modeling, Multispectral imaging, Diffusion, Biological research, Monte Carlo methods

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