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Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Yue Mei1,2,3, Jianwei Deng1,2, Dongmei Zhao1,2, Changjiang Xiao1,2, Tianhang Wang4, Li Dong5, Xuefeng Zhu1,6,*

1 Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116023, China
2 International Research Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, China
3 DUT-BSU Joint Institute, Dalian University of Technology, Dalian, 116023, China
4 Department of Automotive Engineering, Tongji University, Shanghai, 201804, China
5 Amazon.com, Seattle, WA, 98109, USA
6 School of Automotive Engineering, Dalian University of Technology, Dalian, 116023, China

* Corresponding Author: Xuefeng Zhu. Email: email

(This article belongs to the Special Issue: Machine Learning Based Computational Mechanics)

Computer Modeling in Engineering & Sciences 2024, 139(1), 911-935. https://doi.org/10.32604/cmes.2023.043810

Abstract

Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues. The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing. To address this issue, we propose a deep learning (DL) model based on conditional Generative Adversarial Networks (cGANs) to improve the quality of nonhomogeneous shear modulus reconstruction. To train this model, we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution. Both the simulated and experimental displacement fields are used to validate the proposed method. The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method. Moreover, we emphasize that our DL model is only trained on synthetic data. This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography. Overall, this work addresses several fatal issues in applying the DL technique into elastography, and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

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Cite This Article

APA Style
Mei, Y., Deng, J., Zhao, D., Xiao, C., Wang, T. et al. (2024). Toward improved accuracy in quasi-static elastography using deep learning. Computer Modeling in Engineering & Sciences, 139(1), 911-935. https://doi.org/10.32604/cmes.2023.043810
Vancouver Style
Mei Y, Deng J, Zhao D, Xiao C, Wang T, Dong L, et al. Toward improved accuracy in quasi-static elastography using deep learning. Comput Model Eng Sci. 2024;139(1):911-935 https://doi.org/10.32604/cmes.2023.043810
IEEE Style
Y. Mei et al., "Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning," Comput. Model. Eng. Sci., vol. 139, no. 1, pp. 911-935. 2024. https://doi.org/10.32604/cmes.2023.043810



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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