张光磊 - 北京航空航天大学 - 医学科学与工程学院 / 医工交叉创新研究院

个人简介

张光磊，男，北京航空航天大学计划特聘副研究员，生物医学工程高精尖创新中心研究员，博士生导师。2010-2014年在清华大学医学院生物医学工程专业攻读博士学位，2015-2016年在美国斯坦福大学（Stanford University）医学院进行博士后研究工作。2017-2018年任北京交通大学医学智能研究所副教授、副所长。2018年入职北京航空航天大学生物与医学工程学院，获批北航“青年拔尖人才计划”。现任IEEE member、中国生物医学工程学会会员、生物医学光子学分会会员、医学物理分会青年委员、中国图象图形学会会员、中国光学工程学会高级会员。担任Frontiers in Oncology、Frontiers in Neuroscience等杂志编委。 “智能医学实验室”依托北京航空航天大学生物医学工程高精尖中心建立，实验室负责人为张光磊研究员。“智能医学实验室”研究方向包括：光学分子影像三维成像方法、医学影像人工智能分析方法、医学智能可穿戴式诊断设备。“智能医学实验室”科研成果包括：在科研论文方面，在ACS Nano, Nano Energy, IEEE Trans. Med. Imag, IEEE Trans. Biomed. Eng., IEEE J. Biomed. Health Inform., Opt. Lett., Biomed. Opt. Express, Med. Phys., Phys. Med. Biol.等期刊上发表六十余篇论文；在专利软著方面，申请国家发明专利十余项，病理图像AI分析软著一项；在科研项目方面，承担国家自然科学基金、北京市自然科学基金、科技部国家重点研发计划等基金项目。教育经历 2010.9 -- 2014.7 清华大学生物医学工程博士研究生毕业博士 2004.9 -- 2007.4 西北工业大学生物医学工程硕士研究生毕业硕士 2000.9 -- 2004.7 西北工业大学生物医学工程大学本科毕业学士工作经历 2022.1 -- 至今北京航空航天大学生物与医学工程学院特聘副研究员 2018.3 -- 2021.12 北京航空航天大学医工交叉创新研究院特聘副研究员 2017.3 -- 2018.3 北京交通大学医学智能研究所副所长副教授 2015.10 -- 2016.10 美国斯坦福大学（Stanford University）博士后 2014.7 -- 2017.3 北京交通大学计算机与信息技术学院博士后 2007.4 -- 2010.8 深圳迈瑞生物医疗电子股份有限公司资深工程师社会兼职 IEEE Member 中国生物医学工程学会会员生物医学光子学分会会员医学物理分会青年委员中国图象图形学会会员中国光学工程学会高级会员

研究领域

光学分子影像三维成像方法（荧光分子断层成像-FMT、X射线激发荧光分子断层成像-XLCT等）医学影像人工智能分析方法（利用机器学习、深度学习等技术对CT、MRI等医学影像进行智能分析）医学智能可穿戴式诊疗设备（可穿戴式心电、血压、血氧、血糖、呼吸、体温等人体基本生理参数检测设备研究）

近期论文

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W. Yan, C. Ma, X. Cai, Y. Sun, G. Zhang*, and W. Song*, “Self-powered and wireless physiological monitoring system with integrated power supply and sensors,” Nano Energy, 2023, 108, 108203. (SCI, Q1, IF=19.069) X. Zhao, P. Zhang, F. Song, C. Ma, G. Fan, Y. Sun, Y. Feng, and G. Zhang*, “Prior attention network for multi-lesion segmentation in medical images,” IEEE Trans. Med. Imag., 2022, 41(12): 3812–3823. (SCI, IF=11.037) X. Zhang, X. Cao, P. Zhang, F. Song, J. Zhang, L. Zhang, and G. Zhang*, “Self-training strategy based on finite element method for adaptive bioluminescence tomography reconstruction,” IEEE Trans. Med. Imag., 2022, 41(10): 2629–2643. (SCI, IF=11.037) T. Zhang, Y. Feng, Y. Zhao, G. Fan, A. Yang, S. Lyu, P. Zhang, F. Song, C. Ma, Y. Sun, Y. Feng, and G. Zhang*, “MSHT: Multi-stage hybrid transformer for the ROSE image analysis of pancreatic cancer,” IEEE J. Biomed. Health Inform., 2023, in press. (SCI, Q1, IF=7.021) C. Ma, P. Zhang, F. Song, Y. Sun, G. Fan, T. Zhang, Y. Feng, and G. Zhang*, “KD-Informer: cuff-less continuous blood pressure waveform estimation approach based on single photoplethysmography,” IEEE J. Biomed. Health Inform., 2022, in press. (SCI, IF=7.021) P. Zhang, G. Fan, T. Xing, F. Song, and G. Zhang*, “UHR-DeepFMT: Ultra-high spatial resolution reconstruction of fluorescence molecular tomography based on 3D fusion dual-sampling deep neural network,” IEEE Trans. Med. Imag., 2021, 40(11): 3217–3228. (SCI, IF=11.037) L. Guo, F. Liu, C. Cai, J. Liu, and G. Zhang*, “3D deep encoder-decoder network for fluorescence molecular tomography,” Opt. Lett., 2019, 44(8): 1892–1895. (SCI, IF=3.560) G. Zhang*, S. Tzoumas, K. Cheng, F. Liu, J. Liu, J. Luo, J. Bai, and L. Xing, “Generalized adaptive Gaussian Markov random field for X-ray luminescence computed tomography,” IEEE Trans. Biomed. Eng., 2018, 65(9): 2130–2133. (SCI, IF=4.756) G. Zhang*, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam X-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imag., 2017, 36(1): 225–235. (SCI, IF=11.037) G. Zhang, H. Pu, W. He, F. Liu, J. Luo, and J. Bai, “Bayesian framework based direct reconstruction of fluorescence parametric images,” IEEE Trans. Med. Imag., 2015, 34(6): 1378–1391. (SCI, IF=11.037) 科研论文 W. Yan, C. Ma, X. Cai, Y. Sun, G. Zhang*, and W. Song*, “Self-powered and wireless physiological monitoring system with integrated power supply and sensors,” Nano Energy, 2023, 108, 108203. (SCI, Q1, IF=19.069) P. Zhang, F. Song, C. Ma, Z. Liu, H. Wu, Y. Sun, Y. Feng, Y. He, and G. Zhang*, “Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy,” Phys. Med. Biol., 2023, in press. (SCI, Q2, IF=4.174) P. Zhang, C. Ma, F. Song, Y. Sun, Y. Feng, Y. He, T. Zhang, and G. Zhang*, “D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detection,” Biomed. Signal Process. Control, 2023, 82, 104615. (SCI, Q2, IF=5.076) F. Song, X. Song, Y. Feng, G. Fan, Y. Sun, P. Zhang, J. Li, F. Liu, and G. Zhang*, “Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer: a multi-dataset study,” Med. Phys., 2023, in press. (SCI, Q2, IF=4.506) T. Zhang, Y. Feng, Y. Zhao, G. Fan, A. Yang, S. Lyu, P. Zhang, F. Song, C. Ma, Y. Sun, Y. Feng, and G. Zhang*, “MSHT: Multi-stage hybrid transformer for the ROSE image analysis of pancreatic cancer,” IEEE J. Biomed. Health Inform., 2023, in press. (SCI, Q1, IF=7.021) X. Zhang, J. Cui, Y. Jia, P. Zhang, F. Song, X. Cao, J. Zhang, L. Zhang, G. Zhang*, “Image restoration for blurry optical images caused by photon diffusion with deep learning,” J. Opt. Soc. Am. A, 2023, 40(1): 96–107. (SCI, Q3, IF=2.104) P. Zhang, C. Ma, F. Song, T. Zhang, Y. Sun, Y. Feng, Y. He, F. Liu, D. Wang, and G. Zhang*, “Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain,” Comput. Meth. Programs Biomed., 2023, 229, 107293. (SCI, Q1, IF=7.027) C. Ma, P. Zhang, F. Song, Y. Sun, G. Fan, T. Zhang, Y. Feng, and G. Zhang*, “KD-Informer: cuff-less continuous blood pressure waveform estimation approach based on single photoplethysmography,” IEEE J. Biomed. Health Inform., 2022, in press. (SCI, Q1, IF=7.021) X. Zhao, P. Zhang, F. Song, C. Ma, G. Fan, Y. Sun, Y. Feng, and G. Zhang*, “Prior attention network for multi-lesion segmentation in medical images,” IEEE Trans. Med. Imag., 2022, 41(12): 3812–3823. (SCI, Q1, IF=11.037) P. Zhang, C. Ma, F. Song, Z. Liu, Y. Feng, Y. Sun, Y. He, F. Liu, D. Wang, and G. Zhang*, “Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography,” Biomed. Opt. Express, 2022, 13(10): 5327–5343. (SCI, Q2, IF=3.562) F. Liu, P. Zhang, Z. Liu, F. Song, C. Ma, Y. Sun, Y. Feng, Y. He, and G. Zhang*, “In vivo accurate detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography,” J. Biomed. Opt., 2022, 27(7): 070501. (SCI, Q2, IF=3.582) 麻琛彬, 张鹏, 宋凡, 孙洋洋, 张光磊*, “基于光电容积脉搏波的无袖带血压测量技术研究进展,” 北京生物医学工程, 2022, in press. X. Zhang, X. Cao, P. Zhang, F. Song, J. Zhang, L. Zhang, and G. Zhang*, “Self-training strategy based on finite element method for adaptive bioluminescence tomography reconstruction,” IEEE Trans. Med. Imag., 2022, 41(10): 2629–2643. (SCI, Q1, IF=11.037) J. Li, F. Song, P. Zhang, C. Ma, T. Zhang, Y. Sun, Y. Feng, X. Song, S. Lyu, and G. Zhang*, “A multi-classification model for non-small cell lung cancer subtypes based on independent subtask learning,” Med. Phys., 2022, 49: 6969–6974. (SCI, Q2, IF=4.506) P. Zhang, C. Ma, F. Song, G. Fan, Y. Sun, Y. Feng, X. Ma, F. Liu, and G. Zhang*, “A review of advances in imaging methodology in fluorescence molecular tomography,” Phys. Med. Biol., 2022, 67: 10TR01. (SCI, Q2, IF=4.174) Y. Feng, F. Song, P. Zhang, G. Fan, T. Zhang, X. Zhao, C. Ma, Y. Sun, X. Song, H. Pu, F. Liu, and G. Zhang*, “Prediction of EGFR mutation status in non-small cell lung cancer based on ensemble learning,” Front. Pharmaco., 2022,13: 897597. (SCI, Q1, IF=5.988) F. Song, L. Song, T. Xing, X. Song, P. Zhang, Y. Feng, Z. Zhu, W. Song, and G. Zhang*, “A multi-classification model for predicting the invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules,” Front. Oncol., 2022, 12: 800811. (SCI, Q2, IF=5.738) G. Zhang*, X. Chen, S. Wang, J. Li, and X. Cao, “Editorial: Optical Molecular Imaging in Cancer Research,” Front. Oncol., 2022, 12: 870583. (SCI, Q2, IF=5.738) P. Zhang, G. Fan, T. Xing, F. Song, and G. Zhang*, “UHR-DeepFMT: Ultra-high spatial resolution reconstruction of fluorescence molecular tomography based on 3D fusion dual-sampling deep neural network,” IEEE Trans. Med. Imag., 2021, 40(11): 3217–3228. (SCI, Q1, IF=11.037) P. Zhang, C. Ma, Y. Sun, G. Fan, F. Song, Y. Feng, and G. Zhang*, “Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings,” Comput. Biol. Med., 2021, 139: 104880. (SCI, Q1, IF=6.698) X. Zhao, P. Zhang, F. Song, G. Fan, Y. Sun, Y. Wang, Z. Tian, L. Zhang, and G. Zhang*, “D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution,” Comput. Biol. Med., 2021, 135: 104526. (SCI, Q1, IF=6.698) Y. Gao, F. Song, P. Zhang, J. Liu, J. Cui, Y. Ma, G. Zhang*, and J. Luo, “Improving the subtype classification of non-small cell lung cancer by elastic deformation based machine learning,” J. Digit. Imaging, 2021, 34: 605–617. (SCI, Q2, IF=4.903) L. Song, T. Xing, Z. Zhu, W. Han, G. Fan, J. Li, H. Du, W. Song, Z. Jin, and G. Zhang, “Hybrid clinical-radiomics model for precisely predicting the invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule,” Acad. Radiol., 2021, 28(9): e267–e277. (SCI, Q1, IF=5.482) R. Liu, Z. Cai, Q. Zhang, H. Yuan, G. Zhang, and D. Yang, “Colorimetric two-dimensional photonic crystal biosensors for label-free detection of hydrogen peroxide,” Sens. Actuators B., 2021, 354:131236. (SCI, Q1, IF=9.221) R. Zhao, D. Wu, J. Wen, Q. Zhang, G. Zhang, and J. Li, “Robustness and accuracy improvement of data processing with 2D neural networks for transient absorption dynamics,” Phys. Chem. Chem. Phys., 2021, 23: 16998-17008. (SCI, Q1, IF=3.945) W. Cai, Y. Chen, J. Guo, B. Han, Y.Shi, L. Ji, J. Wang, G. Zhang*, and J. Luo, “Accurate detection of atrial fibrillation from 12-Lead ECG using deep neural network,” Comput. Biol. Med., 2020, 116: 103378. (SCI, Q1, IF=4.589) Y. Yuan, W. Qin, B. Ibragimov, G. Zhang, B. Han, M. Q.-H. Meng, L. Xing, “Densely connected neural network with unbalanced discriminant and category sensitive constraints for polyp recognition,” IEEE Trans. Autom. Sci. Eng., 2020, 17(2): 574–583. (SCI, Q1, IF=5.083) Y. Li, Y. Liu, M. Zhang, G. Zhang, Z. Wang, and J. Luo, “Radiomics with attribute bagging for breast tumor classification using multimodal ultrasound images,” J. Ultras. Med., 2020, 39(2): 361–371. (SCI, Q2, IF=2.153) L. Guo, F. Liu, C. Cai, J. Liu, and G. Zhang*, “3D deep encoder-decoder network for fluorescence molecular tomography,” Opt. Lett., 2019, 44(8): 1892–1895. (SCI, Q1, IF=3.776) J. Liu, J. Cui, F. Liu, Y. Yuan, F. Guo, and G. Zhang*, “Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model,” Med. Phys., 2019, 46(7): 3091–3100. (SCI, Q1, IF=4.071) L. Zhang, and G. Zhang*, “Brief review on learning based methods for optical tomography,” J. Innov. Opt. Heal. Sci., 2019, 12(6): 1930011. (SCI, Q3, IF=1.661) S. Jiang, J. Liu, G. Zhang, Y. An, H. Meng, Y. Gao, K. Wang, and J. Tian, “Reconstruction of fluorescence molecular tomography via a fused LASSO method based on group sparsity prior,”IEEE Trans. Biomed. Eng., 2019, 66(5): 1361–1371. (SCI, Q1, IF=4.424) Y. Liu, S. Jiang, J. Liu, Y. An, G. Zhang, Y. Gao, K. Wang, and J. Tian, “Reconstruction method for fluorescence molecular tomography based on L1-norm primal accelerated proximal gradient,” J. Biomed. Opt., 2018, 23(8):085002. (SCI, Q2, IF=2.785) G. Zhang*, S. Tzoumas, K. Cheng, F. Liu, J. Liu, J. Luo, J. Bai, and L. Xing, “Generalized adaptive Gaussian Markov random field for X-ray luminescence computed tomography,” IEEE Trans. Biomed. Eng., 2018, 65(9): 2130–2133. (SCI, Q1, IF=4.424) K. Cheng, M. Sano, C. H. Jenkins, G. Zhang, D. Vernekohl, W. Zhao, C. Wei, Y. Zhang, Z. Zhang, Y. Liu, Z. Cheng, and L. Xing, “Synergistically enhancing the therapeutic effect of radiation therapy with radiation activatable and reactive oxygen species-releasing nanostructures,” ACS Nano, 2018, 12: 4946?4958. (SCI, Q1, IF=14.588) K. Cheng, H. Chen, C. H. Jenkins, G. Zhang, W. Zhao, Z. Zhang, F. Han, J. Fung, M. Yang, Y. Jiang, L. Xing, and Z. Cheng, “Synthesis, characterization, and biomedical applications of a targeted dual-modal near-infrared-II fluorescence and photoacoustic imaging nanoprobe,” ACS Nano, 2017, 11:12276–12291. (SCI, Q1, IF=14.588) G. Zhang*, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam X-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imag., 2017, 36(1): 225–235. (SCI, Q1, IF=6.685) G. Zhang, H. Pu, W. He, F. Liu, J. Luo, and J. Bai, “Bayesian framework based direct reconstruction of fluorescence parametric images,” IEEE Trans. Med. Imag., 2015, 34(6): 1378–1391. (SCI, Q1, IF=6.685) G. Zhang, W. He, H. Pu, F. Liu, M. Chen, J. Bai and J. Luo, “Acceleration of dynamic fluorescence molecular tomography with principal component analysis,” Biomed. Opt. Express, 2015, 6(6): 2036–2055. (SCI, Q1, IF=3.921) G. Zhang, H. Pu, W. He, F. Liu, J. Luo, and J. Bai, “Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography,” Appl. Phys. Lett., 2015, 106(8): 081110. (SCI, Q1, IF=3.597) G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A direct method with structural priors for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography,” IEEE Trans. Biomed. Eng., 2014, 61(3): 986–990. (SCI, Q1, IF=4.424) G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt., 2013, 18(4): 040505. (SCI, Q2, IF=2.785) G. Zhang, X. Cao, B. Zhang, F. Liu, J. Luo, and J. Bai, “MAP estimation with structural priors for fluorescence molecular tomography,” Phys. Med. Biol., 2013, 58(2): 351–372. (SCI, Q2, IF=2.883) W. He#, G. Zhang#, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt., 2014, 19(5): 056012. (SCI, Q2, IF=2.785, co-first author) W. He#, G. Zhang#, F. Liu, X. Cao, J. Luo, and J. Bai, “Projected restarted framework for tomographic reconstruction,” Proc. of SPIE, 2014, 9230: 92300F. (EI, co-first author) Y. An, J. Liu, G. Zhang, S. Jiang, J. Ye, C. Chi, and J. Tian, “Compactly supported radial basis function-based meshless method for photon propagation model of fluorescence molecular tomography,” IEEE Trans. Med. Imag., 2017, 36(2): 366–373. (SCI, Q1, IF=6.685) Y. Liu, J. Liu, Y. An, S. Jiang, J. Ye, Y. Mao, K. He, G. Zhang, C. Chi, J. Tian, “Novel trace norm regularization method for fluorescence molecular tomography reconstruction,” Proc. of SPIE, 2017, 10047: 100470U. (EI) S. Jiang, J. Liu, Y. An, G. Zhang, J. Ye, Y. Mao, K. He, C. Chi, and J. Tian, “Novel L2,1-norm optimization method for fluorescence molecular tomography reconstruction,” Biomed. Opt. Express, 2016, 7(6):2342–2359. (SCI, Q1, IF=3.921) Y. An, J. Liu, G. Zhang, J. Ye, Y. Mao, S. Jiang, W. Shang, Y. Du, C. Chi, and J. Tian, “Meshless reconstruction method for fluorescence molecular tomography based on compactly supported radial basis function,” J. Biomed. Opt., 2015, 20(10):105003. (SCI, Q2, IF=2.785) Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, C. Chi, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng., 2015, 62(7): 1818–1826. (SCI, Q1, IF=4.424) X. Zhang, F. Liu, S. Zuo, J. Shi, G. Zhang, J. Bai, and J. Luo, “Reconstruction of fluorophore concentration variation in dynamic fluorescence molecular tomography,” IEEE Trans. Biomed. Eng., 2015, 62(1): 138–144. (SCI, Q1, IF=4.424) H. Pu, G. Zhang, W. He, F. Liu, H. Guang, Y. Zhang, J. Bai, and J. Luo, “Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis,” Phys. Med. Biol., 2014, 59(17): 5025–5042. (SCI, Q2, IF=2.883) W. He, H. Pu, G. Zhang, X. Cao, B. Zhang, F. Liu, J. Luo, and J. Bai, “Subsurface fluorescence molecular tomography with prior information,” Appl. Opt., 2014, 53(3): 402–409. (SCI, Q3, IF=1.961) J. Shi, F. Liu, G. Zhang, B. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt., 2014, 19(4): 046018. (SCI, Q2, IF=2.785) H. Pu, W. He, G. Zhang, B. Zhang, F. Liu, Y. Zhang, J. Luo, and J. Bai, “Separating structures of different fluorophore concentrations by principal component analysis on multispectral excitation-resolved fluorescence tomography images,” Biomed. Opt. Express, 2013, 4(10): 1829–1845. (SCI, Q1, IF=3.921)