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Biomedical Optics Express|August 30, 2021
Quantification of blood flow index in diffuse correlation spectroscopy using long short-term memory architectureZhe Li, Qisi Ge, Jinchao Feng, et al.Journal of Biomedical Optics|December 21, 2018
Back-propagation neural network-based reconstruction algorithm for diffuse optical tomographyJinchao Feng, Qiuwan Sun, Zhe Li, et al.Biomedical Optics Express|October 5, 2020
End-to-end Res-Unet based reconstruction algorithm for photoacoustic imagingJinchao Feng, Jianguang Deng, Zhe Li, et al.Journal of Biophotonics|November 10, 2017
Bayesian sparse-based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational timeJinchao Feng, Kebin Jia, Zhe Li, et al.Computational and Mathematical Methods in Medicine|February 13, 2013
Improved reconstruction quality of bioluminescent images by combining SP(3) equations and Bregman iteration methodQiang Wu, Jinchao Feng, Kebin Jia, et al.Biomedical Optics Express|March 6, 2023
Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomographyWenqian Zhang, Ting Hu, Zhe Li, et al.Optica|March 28, 2022
Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomographyJinchao Feng, Wanlong Zhang, Zhe Li, et al.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 19, 2012
Total variation regularization for bioluminescence tomography with an adaptive parameter choice approachJinchao Feng, Xiaowei Jia, Kebin Jia, et al.Applied Optics|August 12, 2025
Multiscale local sparsity and prior learning algorithm for Cherenkov-excited luminescence scanned tomography reconstructionHu Zhang, Ting Hu, Mengfan Geng, et al.Journal of Biomedical Optics|February 23, 2023
X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality moduleJinchao Feng, Hu Zhang, Mengfan Geng, et al.Pageof 1,126