1.M．Lu, W. Shi, Z. Jiang, B. Li, D. Ta, and X. Liu*, “Deep learning method for cell count from transmitted-light microscope,” J. Innov. Opt. Heal. Sci., 2350004, 2023.
2.X. Liu, B. Li, C. Liu, and D. Ta*, “Virtual fluorescence translation for biological tissue by conditional generative adversarial network,” Phenomics, 408-420, 2023.
3.W. Gu, B. Li, J. Luo, Z. Yan*, D. Ta, and X. Liu*, “Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network”, IEEE Trans. Ultrason. Ferr. Freq. Control, 25-40, 2023.
4.Y. Song, M. Lu, Y. Xie, G. Sun, J. Chen, H. Zhang,X. Liu,* F. Zhang, and L. Sun*, “Deep Learning Fluorescence Imaging of Visible to NIR-II Based on Modulated Multimode Emissions Lanthanide Nanocrystals,” Advanced Functional Materials, 2206802, 2022.
5.B. Li, M. Lu, C. Liu,X. Liu*, and D. Ta, “Acoustic hologram reconstruction with unsupervised neural network,” Frontiers in Materials 9, 916527, 2022.
6.H．Shahid, A. Khalid, Y. Yue,X. Liu*, D. Ta*,” A Feasibility Study of Generative Adversarial Network for Artifacts Removal in Experimental Photoacoustic Imaging,” Ultrasound in Medicine & Biology, 48(8):1628-1643, 2022.
7.Y. Yue, N. Li, H. Shahid, D. Bi,X. Liu*, S. Song*, and D. Ta, “Gross tumor volume definition and comparative assessment for esophageal squamous cell carcinoma from 3D 18F-FDG PET/CT by deep learning-based method,” Frontiers in Oncology 17, 799207, 2022.
8.X. Liu*,#, B. Li#, B. Pang, C. Liu, Y. Shu, K. Xu, J. Luo, and D. Ta*, “Improved Ultrasound Imaging Performance with Complex Cumulant Analysis,” IEEE Trans. Biomed. Engineering 69, 1281-1289, 2022.
9.Z. Jiang, B. Li, Tho. Tran, J. Jiang,X. Liu*, and D. Ta*, “Fluo-Fluo translation based on deep learning,” Chi. Opt. Letters 20, 031701, 2022.
10.X. Liu*, T. Zhou, M. Lu, Y. Yang, Q. He, and J. Luo*, “Deep learning for ultrasound localization microscopy,” IEEE Trans. Med. Imaging 39: 3064-3078, 2020.
11.Y. Liu, Y. Yang, Y. Shu, T. Zhou, J. Luo, andX. Liu*, “Super-Resolution Ultrasound Imaging by Sparse Bayesian Learning Method,” IEEE ACCESS 7, 47197-47205, 2019.
12.X. Liu*, X. Tang, Y. Shu, L. Zhao, Y. Liu, and T. Zhou, “Single-view cone-beam x-ray luminescence optical tomography based on Group_YALL1 method,” Phys. Med. Biol. 64, 2019.
13.Y. Shu, C. Han, M. Lv, and X. Liu*, “Fast super-resolution ultrasound imaging with compressed sensing reconstruction method and single plane wave transmission,” IEEE ACCESS 6, 39298-39306, 2018.
14.L. Zhao, C. Han, Y. Shu, M. Lv, Y. Liu, T. Zhou, Z. Yan, and X. Liu*, “Improved imaging performance in super-resolution localization microscopy by YALL1 method,” IEEE ACCESS 6, 5438-5446, 2018.
15.Y. Shu, L. Zhao, J. Jiang, Z. Yan, J. Luo*, and X. Liu*, “Research progress of X-ray luminescence optical tomography,”科学通报, 62, 3838-3850, 2017.
16.X. Liu*, H. Wang, and Z. Yan, “Non-stationary reconstruction for dynamic fluorescence molecular tomography with extended kalman filter,” Bio. Opt. Express 7, 4527-4542, 2016.
17.X. Liu*, Q. Liao, H. Wang, and Z. Yan, “Excitation-resolved cone-beam x-ray luminescence tomography,” J. Biomed. Opt. 20, 070501, 2015.
18.X. Liu*, X. He, Z. Yan, and H. Lu, “4-D reconstruction of fluorescence molecular tomography using re-assembled measurement data,” Bio. Opt. Express 6, 1963-1976, 2015.
19.X. Liu*, X. He and Z. Yan, “Performance evaluation of principal component analysis for dynamic fluorescence tomographic imaging in measurement space,” Opt. Engineering 54, 053108, 2015.
20.X. Liu, Z. Yan and H. Lu*, “Performance evaluation of a priori information on reconstruction of fluorescence molecular tomography,” IEEE ACCESS 3, 64-72, 2015.
21.X. Liu*, Q. Liao, and H. Wang, “Fast X-ray Luminescence Computed Tomography Imaging,” IEEE Trans. Biomed. Eng. 61, 1621-1627, 2014.
22.X. Liu*, Q. Liao, and H. Wang, “In vivo x-ray luminescence tomographic imaging with single view data,” Opt. Letters 38, 4530–4533, 2013.
23.X. Liu, B. Zhang, J. Luo, and J. Bai*, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31, 2120–2132, 2012.
24.X. Liu, B. Zhang, J. Luo, and J. Bai*, “Principal component analysis of dynamic fluorescence tomography in measurement space,” Phys. Med. Biol. 57, 2727–2742, 2012.
25.X. Liu, F. Liu, Y. Zhang, and J. Bai*, “Unmixing dynamic fluorescence diffuse optical tomography images with independent component analysis,” IEEE Trans. Med. Imaging 30, 1591–1604, 2011.
26.X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai*, “Imaging of indocyanine green perfusion in mouse liver with fluorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58, 2139–2143, 2011.
27.X. Liu, F. Liu, and J. Bai*, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58, 1602–1611, 2011.
28.X. Guo,X. Liu,X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai*, “A combined fluorescence and micro-computed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57, 2876–2883, 2010. (contribute equally)
29.X. Liu, F. Liu, D. Wang, and J. Bai*, “In vivo whole-body imaging of optical agent dynamics using full angle fluorescence diffuse optical tomography,” Chin. Opt. Lett. 8, 1156–1159, 2010.
30.X. Liu, D. Wang, F. Liu, and J. Bai*, “Principal component analysis of dynamic fluorescence diffuse optical tomography images,” Opt. Express 18, 6300–6314, 2010.
