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Your search keyword '"Zheng, Zesheng"' showing total 36 results
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36 results on '"Zheng, Zesheng"'

1. HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network

Author

Zhang, Zhengyuan, Jin, Haoran, Zheng, Zesheng, Zhang, Wenwen, Lu, Wenhao, Qin, Feng, Sharma, Arunima, Pramanik, Manojit, and Zheng, Yuanjin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics, Physics - Optics
Abstract

Photoacoustic microscopy (PAM) is a novel implementation of photoacoustic imaging (PAI) for visualizing the 3D bio-structure, which is realized by raster scanning of the tissue. However, as three involved critical imaging parameters, imaging speed, lateral resolution, and penetration depth have mutual effect to one the other. The improvement of one parameter results in the degradation of other two parameters, which constrains the overall performance of the PAM system. Here, we propose to break these limitations by hardware and software co-design. Starting with low lateral resolution, low sampling rate AR-PAM imaging which possesses the deep penetration capability, we aim to enhance the lateral resolution and up sampling the images, so that high speed, super resolution, and deep penetration for the PAM system (HSD-PAM) can be achieved. Data-driven based algorithm is a promising approach to solve this issue, thereby a dedicated novel dual branch fusion network is proposed, which includes a high resolution branch and a high speed branch. Since the availability of switchable AR-OR-PAM imaging system, the corresponding low resolution, undersample AR-PAM and high resolution, full sampled OR-PAM image pairs are utilized for training the network. Extensive simulation and in vivo experiments have been conducted to validate the trained model, enhancement results have proved the proposed algorithm achieved the best perceptual and quantitative image quality. As a result, the imaging speed is increased 16 times and the imaging lateral resolution is improved 5 times, while the deep penetration merit of AR-PAM modality is still reserved.

Published
2023

15. Adaptive Enhancement of Acoustic Resolution Photoacoustic Microscopy Imaging Via Deep CNN Prior

Author

Zhang, Zhengyuan, Jin, Haoran, Zhang, Wenwen, Lu, Wenhao, Zheng, Zesheng, Sharma, Arunima, Pramanik, Manojit, Zheng, Yuanjin, and School of Electrical and Electronic Engineering

Subjects
History, Deep Learning, Editorial, Polymers and Plastics, Electrical and electronic engineering [Engineering], Acoustic Resolution, Radiology, Nuclear Medicine and imaging, Business and International Management, Atomic and Molecular Physics, and Optics, Industrial and Manufacturing Engineering
Abstract

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm. Ministry of Education (MOE) Published version This research is supported by the Ministry of Education, Singapore, under its MOE ARF Tier 2 (Award no. MOE2019-T2-2-179).

Published
2022

36. Electromagnetic⁻Acoustic Sensing for Biomedical Applications.

Author

Liu S, Zhang R, Zheng Z, and Zheng Y

Subjects
Humans, Lasers, Magnetics, Microwaves, Ultrasonography, X-Rays, Acoustics, Biomedical Research methods, Clinical Medicine methods, Electromagnetic Phenomena
Abstract

This paper reviews the theories and applications of electromagnetic⁻acoustic (EMA) techniques (covering light-induced photoacoustic, microwave-induced thermoacoustic, magnetic-modulated thermoacoustic, and X-ray-induced thermoacoustic) belonging to the more general area of electromagnetic (EM) hybrid techniques. The theories cover excitation of high-power EM field (laser, microwave, magnetic field, and X-ray) and subsequent acoustic wave generation. The applications of EMA methods include structural imaging, blood flowmetry, thermometry, dosimetry for radiation therapy, hemoglobin oxygen saturation (SO₂) sensing, fingerprint imaging and sensing, glucose sensing, pH sensing, etc. Several other EM-related acoustic methods, including magnetoacoustic, magnetomotive ultrasound, and magnetomotive photoacoustic are also described. It is believed that EMA has great potential in both pre-clinical research and medical practice.

Published
2018
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