Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human

被引:9
|
作者
Zheng, Wenhan [1 ]
Zhang, Huijuan [1 ]
Huang, Chuqin [1 ]
Shijo, Varun [1 ,2 ]
Xu, Chenhan [2 ]
Xu, Wenyao [2 ]
Xia, Jun [1 ,2 ]
机构
[1] SUNY Buffalo, Dept Biomed Engn, New York, NY 14260 USA
[2] SUNY Buffalo, Dept Comp Sci & Engn, New York, NY 14260 USA
基金
美国国家卫生研究院;
关键词
3D vascular Imaging; deep learning; linear transducer arrays; photoacoustic tomography; COMPUTED-TOMOGRAPHY; RECONSTRUCTION;
D O I
10.1002/advs.202301277
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The development of high-performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully-dense (3DFD) U-net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U-net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.
引用
收藏
页数:12
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