张光磊，男，北航“医工百人”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。2014年获清华大学生物医学工程专业博士学位，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医工交叉创新研究院。在教学方面，曾担任“面向对象的程序设计”及“数值计算方法”本科生课程主讲教师、“算法设计与分析”研究生课程主讲教师。在科研项目方面，主持国家自然科学基金、北京市自然科学基金、中国博士后基金、中央高校基本科研业务费等多项科研项目。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疗设备。在ACS Nano, IEEE Trans. Med. Imag., IEEE Trans. Biomed. Eng., Appl. Phys. Lett., Opt. Lett., Biomed. Opt. Express, J. Biomed. Opt., Med. Phys., Phys. Med. Biol.等期刊上发表三十余篇论文，另外获得2014年度“中国专利优秀奖”。现任职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、中国图象图形学学会会员。

科研论文：

[1]      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, in press. (SCI, Q1, IF=6.685)

[2]      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, in press. (SCI, Q1, IF=3.434)

[3]      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, in press. (SCI, Q1, IF=3.697)

[4]      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., 2020, in press. (SCI, Q2, IF=2.488)

[5]      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=3.434)

[6]      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=4.938)

[7]      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=1.759)

[8]      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=3.714)

[9]      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=3.317)

[10]  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)

[11]  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)

[12]  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)

[13]  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)

[14]  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)

[15]  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)

[16]  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)

[17]  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)

[18]  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)

[19]  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)

[20]  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)

[21]  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)

[22]  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)

[23]  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)

[24]  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)

[25]  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)

[26]  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)

[27]  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)

[28]  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)

[29]  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)

[30]  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)

[31]  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)

[32]  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)

[33]  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)

[34]  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)