Yue Mei1,2,3, Jianwei Deng1,2, Dongmei Zhao1,2, Changjiang Xiao1,2, Tianhang Wang4, Li Dong5, Xuefeng Zhu1,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 911-935, 2024, DOI: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 More >
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