Deep-e: a fully-dense neural network for improving the elevation resolution in linear-array-based photoacoustic tomography

Huijuan Zhang; Wei Bo; Depeng Wang; Anthony DiSpirito III; Chuqin Huang; Nikhila Nyayapathi; Emily Zheng; Tri Vu; Yiyang Gong; Junjie Yao; Wenyao Xu; Jun Xia

doi:10.1117/12.2610814

4 April 2022 Deep-e: a fully-dense neural network for improving the elevation resolution in linear-array-based photoacoustic tomography

Huijuan Zhang, Wei Bo, Depeng Wang, Anthony DiSpirito III, Chuqin Huang, Nikhila Nyayapathi, Emily Zheng, Tri Vu, Yiyang Gong, Junjie Yao, Wenyao Xu, Jun Xia

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203222 (2022) https://doi.org/10.1117/12.2610814
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

A unique deep learning network, Deep-E, is proposed, which utilizes 2D training data to solve a 3D problem. The novelty of this simulation method is to generate a 2D matrix in the axial-elevational plane using an arc-shaped transducer element, instead of generating a 3D matrix using the linear transducer arrays. Deep-E exhibited significant resolution improvement on the in vivo human breast data. In addition, we were able to restore deeper vascular structures and remove the noise artifact. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.

Conference Presentation

Citation Download Citation

Huijuan Zhang, Wei Bo, Depeng Wang, Anthony DiSpirito III, Chuqin Huang, Nikhila Nyayapathi, Emily Zheng, Tri Vu, Yiyang Gong, Junjie Yao, Wenyao Xu, and Jun Xia "Deep-e: a fully-dense neural network for improving the elevation resolution in linear-array-based photoacoustic tomography", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203222 (4 April 2022); https://doi.org/10.1117/12.2610814

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