中国物理B ›› 2022, Vol. 31 ›› Issue (2): 24302-024302.doi: 10.1088/1674-1056/ac0dab

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Deep learning for image reconstruction in thermoacoustic tomography

Qiwen Xu(徐启文)1, Zhu Zheng(郑铸)2, and Huabei Jiang(蒋华北)3,†   

  1. 1 School of Electronic Science and Engineering(National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu 611731, China;
    2 School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    3 Department of Medical Engineering, University of South Florida, Tampa, FL 33620, USA
  • 收稿日期:2021-04-30 修回日期:2021-06-11 接受日期:2021-06-23 出版日期:2022-01-13 发布日期:2022-01-26
  • 通讯作者: Huabei Jiang E-mail:hjiang1@usf.edu

Deep learning for image reconstruction in thermoacoustic tomography

Qiwen Xu(徐启文)1, Zhu Zheng(郑铸)2, and Huabei Jiang(蒋华北)3,†   

  1. 1 School of Electronic Science and Engineering(National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu 611731, China;
    2 School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    3 Department of Medical Engineering, University of South Florida, Tampa, FL 33620, USA
  • Received:2021-04-30 Revised:2021-06-11 Accepted:2021-06-23 Online:2022-01-13 Published:2022-01-26
  • Contact: Huabei Jiang E-mail:hjiang1@usf.edu

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摘要： Microwave-induced thermoacoustic tomography (TAT) is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging. While an image reconstruction algorithm is critical for the TAT, current reconstruction methods often creates significant artifacts and are computationally costly. In this work, we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image. We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy. For the scenarios where realistic training data are scarce or unavailable, we use the finite element method (FEM) to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method. The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness. Compared with other state-of-the-art reconstruction methods, the TAT-Net method can reduce the root mean square error to 0.0143, and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64, respectively. The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.

关键词: thermoacoustic tomography (TAT), TAT reconstruction, deep learning, finite-element

Abstract: Microwave-induced thermoacoustic tomography (TAT) is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging. While an image reconstruction algorithm is critical for the TAT, current reconstruction methods often creates significant artifacts and are computationally costly. In this work, we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image. We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy. For the scenarios where realistic training data are scarce or unavailable, we use the finite element method (FEM) to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method. The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness. Compared with other state-of-the-art reconstruction methods, the TAT-Net method can reduce the root mean square error to 0.0143, and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64, respectively. The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.

Key words: thermoacoustic tomography (TAT), TAT reconstruction, deep learning, finite-element

中图分类号:  (Thermoacoustics, high temperature acoustics, photoacoustic effect)

  • 43.35.Ud
87.85.Pq (Biomedical imaging) 87.57.nf (Reconstruction) 84.35.+i (Neural networks)