张光磊，男，北京航空航天大学生物与医学工程学院副教授、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“医工百人计划”、“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。

“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技术、医学影像人工智能分析方法、医疗可穿戴设备。“智能医学实验室”科研成果包括：在科研论文方面，在IEEE TMI, IEEE TBME, IEEE TCI, IEEE TIM, IEEE TAI, IEEE JBHI, OL, BOE, MP, PMB, Nano Energy, ACS Nano等期刊上发表八十余篇论文；在专利软著方面，申请国家发明专利二十余项，获批病理图像AI分析软著一项，“中国专利优秀奖”一项；在科研项目方面，承担国家重点研发计划、国家自然科学基金、北京市自然科学基金等十余项基金项目；在分子影像系统方面，搭建了多模态超分辨荧光分子三维成像系统，可实现近红外I、II区多谱段荧光分子成像，突破了传统成像系统的空间分辨率瓶颈；在医疗可穿戴设备方面，研发了便携式智能心电卡、无袖带光电血压计、无创血糖仪、人体多参数智能监测仪等设备。

 

代表性论文（生物医学影像）： 

 

[1]      Y. He, Z. Liu, M. Qi, S. Ding, P. Zhang, F. Song, C. Ma, H. Wu, R. Cai, Y. Feng, H. Zhang, T. Zhang, and G. Zhang*, “PST-Diff: Achieving high-consistency stain transfer by diffusion models with pathological and structural constraints,” IEEE Trans. Med. Imag., 2024, in press. (SCI, IF=10.6)

[2]      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=10.6)

[3]      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=10.6)

[4]      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=10.6)

[5]      P. Zhang, C. Ma, F. Song, Z. Liu, H. Wu, Y. Feng, Y. He, D. Wang, G. Zhang*, “SVRNet: First investigation of single-view reconstruction network for fluorescence molecular tomography,” IEEE Trans. Comput. Imag., 2023, 9: 834–845. (SCI, Q1, IF=5.4)

[6]      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=3.6)

[7]      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=10.6)

[8]      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=10.6)

[9]      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, 27(4): 1946–1957. (SCI, Q1, IF=7.7) (Featured Article)

[10]  Z. Liu, T. Zhang, Y., and G. Zhang*, “Generating Progressive Images from Pathological Transitions via Diffusion Model,” MICCAI, 2024.

  

代表性论文（医疗可穿戴设备）： 

 

[1]     C. Ma, L. Guo, H. Zhang, Z. Liu, G. Zhang*, “DiffCNBP: Lightweight diffusion model for IoMT-based continuous cuffless blood pressure waveform monitoring using PPG,” IEEE Internet of Things Journal, 2024, in press. (SCI, Q1, IF=8.2)

[2]     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, IF=17.6)

[3]     C. Ma, P. Zhang, F. Song, Z. Liu, Y. Feng, Y. He, and G. Zhang*, “UPR-BP: Unsupervised Photoplethysmography Representation Learning for Noninvasive Blood Pressure Estimation,” IEEE Trans. Artif. Intell., 2024, in press.

[4]     C. Ma, P. Zhang, H. Zhang, Z. Liu, F. Song, Y. He, and G. Zhang*, “STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography,” Expert Syst. Appl., 2024, 249: 123809. (SCI, IF=8.5)

[5]     C. Ma, Y. Sun, P. Zhang, F. Song, Y. Feng, Y. He, and G. Zhang*, “SMART-BP: Sem-Resnet and auto-regressor based on a two-stage framework for noninvasive blood pressure measurement,” IEEE Trans. Instrum. Meas., 2023, in press. (SCI, IF=5.6)

[6]     C. Ma, Y. Xu, P. Zhang, F. Song, Y. Sun, Y. Feng, Y. He, G. Zhang*, “PPG-based continuous BP waveform estimation using polarized attention-guided conditional adversarial learning model,” IEEE J. Biomed. Health Inform., 2023, in press. (SCI, IF=7.7)

[7]     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., 2023, 27(5): 2219–2230. (SCI, IF=7.7)

[8]     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, IF=5.1)

[9]     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, IF=7.7)

[10]  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, IF=7.7)

科研论文： 

     

[1]     C. Ma, L. Guo, H. Zhang, Z. Liu, G. Zhang*, “DiffCNBP: Lightweight diffusion model for IoMT-based continuous cuffless blood pressure waveform monitoring using PPG,” IEEE Internet of Things Journal, 2024, in press. (SCI, Q1, IF=8.2)

[2]     Y. He, Z. Liu, M. Qi, S. Ding, P. Zhang, F. Song, C. Ma, H. Wu, R. Cai, Y. Feng, H. Zhang, T. Zhang, and G. Zhang*, “PST-Diff: Achieving high-consistency stain transfer by diffusion models with pathological and structural constraints,” IEEE Trans. Med. Imag., 2024, in press. (SCI, Q1, IF=10.6)

[3]     Z. Liu, T. Zhang, Y., and G. Zhang*, “Generating progressive images from pathological transitions via diffusion model,” MICCAI, 2024.

[4]     X. Zhang, X. Cao, J. Zhang, L. Zhang, and G. Zhang*, “Neural-field-based image reconstruction for bioluminescence tomography,” J. Innov. Opt. Heal. Sci., 2024, in press. (SCI, Q2, IF=2.3)

[5]     C. Ma, P. Zhang, F. Song, Z. Liu, Y. Feng, Y. He, and G. Zhang*, “UPR-BP: Unsupervised Photoplethysmography Representation Learning for Noninvasive Blood Pressure Estimation,” IEEE Trans. Artif. Intell., 2024, in press. (SCI)

[6]     C. Ma, P. Zhang, H. Zhang, Z. Liu, F. Song, Y. He, and G. Zhang*, “STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography,” Expert Syst. Appl., 2024, 249: 123809. (SCI, Q1, IF=8.5)

[7]     Q. Shi, F. Song, X. Zhou, X. Chen, J. Cao, J. Na, Y. Fan, G. Zhang*, L. Zheng*, “Early predicting osteogenic differentiation of mesenchymal stem cells based on deep learning within one day,” Ann. Biomed. Eng., 2024, 52 (6): 1706–1718. (SCI, Q1, IF=3.8)

[8]     C. Ma, Y. Xu, P. Zhang, F. Song, Y. Sun, Y. Feng, Y. He, and G. Zhang*, “PPG-based continuous BP waveform estimation using polarized attention-guided conditional adversarial learning model,” IEEE J. Biomed. Health Inform., 2023, in press. (SCI, Q1, IF=7.7)

[9]     F. Song, J. Tian, P. Zhang, C. Ma, Y. Sun, Y. Feng, T. Zhang, Y. Lei, Y. He, Z. Cai, Y. Cheng, and G. Zhang*, “A novel feature engineering method based on latent representation learning for radiomics: Application in NSCLC subtype classification,” IEEE J. Biomed. Health Inform., 2024, 28(1): 31–41. (SCI, Q1, IF=7.7)

[10]  C. Ma, Y. Sun, P. Zhang, F. Song, Y. Feng, Y. He, and G. Zhang*, “SMART-BP: Sem-Resnet and auto-regressor based on a two-stage framework for noninvasive blood pressure measurement,” IEEE Trans. Instrum. Meas., 2024, 73: 2503718. (SCI, Q1, IF=5.6)

[11]  T. Zhang, Y. Feng, Y. Zhao, Y. Lei, N. Ying, F. Song, Y. He, Z. Yan, Y. Feng, A. Yang, and G. Zhang*, “SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification,” Comput. Meth. Programs Biomed., 2024, 244: 107969. (SCI, Q1, IF=6.1)

[12]  P. Zhang, C. Ma, F. Song, Z. Liu, H. Wu, Y. Feng, Y. He, D. Wang, G. Zhang*, “SVRNet: First investigation of single-view reconstruction network for fluorescence molecular tomography,” IEEE Trans. Comput. Imag., 2023, 9: 834–845. (SCI, Q1, IF=5.4)

[13]  X. Zhang, Y. Jia, J. Cui, J. Zhang, X. Cao, L. Zhang, G. Zhang*, “Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction,” J. Opt. Soc. Am. A, 2023, 40(7): 1359–1371. (SCI, Q3, IF=1.9)

[14]  Y. He, F. Song, W. Wu, S. Tian, T. Zhang, S. Zhang, P. Zhang, C. Ma, Y. Feng, R. Yang, G. Zhang*, “MultiTrans: Multi-scale feature fusion Transformer with transfer learning strategy for multiple organs segmentation of head and neck CT images,” Med. Nov. Technol. Devices, 2023, 18: 100235.

[15]  G. Zhang*, X. Ma, W. Qin, M. Jia, M. Chen, “Editorial: Optical Imaging in Neuroscience and Brain Disease,” Front. Neurosci., 2023, 17: 1192863. (SCI, Q2, IF=4.3)

[16]  Y. He, P. Xu, H. Wu, Y. Chu, and G. Zhang*, “The model of electrified cell clusters in biological tissues basing on the resting potential difference,” Med. Nov. Technol. Devices, 2023, 18: 100281.

[17]  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=17.6)

[18]  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, 68: 04LT01. (SCI, Q2, IF=3.5)

[19]  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=5.1)

[20]  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, 50(7): 4351–4365. (SCI, Q2, IF=3.8)

[21]  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, 27(4): 1946–1957. (SCI, Q1, IF=7.7) (Featured Article)

[22]  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)

[23]  P. Zhang, C. Ma, F. Song, T. Zhang, Y. Sun, Y. Feng, Y. He, F. Liu, D. Wang, and G. Zhang*, “D2-RecST: 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)

[24]  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., 2023, 27(5): 2219–2230. (SCI, Q1, IF=7.021)

[25]  H. Li, Y. Liu, X. Liang, Y. Yuan, Y. Cheng, G. Zhang, S. Tamura, “Multi-object tracking via deep feature fusion and association analysis,” Eng. Appl. Artif. Intel., 2023, 124: 106527. (SCI, Q1, IF=8.0)

[26]  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)

[27]  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)

[28]  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)

[29]  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, 2023, 42(2): 194–203.

[30]  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)

[31]  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)

[32]  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)

[33]  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)

[34]  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)

[35]  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)

[36]  W. Zhao, G. Zhang, J. Li, “Accuracy improvement of demodulating the stress field with StressUnet in photoelasticity,” Appl. Opt., 2022, 61(29): 8678–8687. (SCI, Q3, IF=1.905)

[37]  P. Zhang, F. Song, C. Ma, Z. Liu, and G. Zhang*, “Multi-attention prior based residual encoder-decoder network for fast and accurate reconstruction in fluorescence molecular tomography,” Proc. SPIE, 2022, 12506: 125063F.

[38]  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)

[39]  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)

[40]  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)

[41]  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)

[42]  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)

[43]  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)

[44]  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)

[45]  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)

[46]  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)

[47]  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)

[48]  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)

[49]  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)

[50]  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)

[51]  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)

[52]  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)

[53]  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)

[54]  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)

[55]  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)

[56]  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)

[57]  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)

[58]  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)

[59]  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)

[60]  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)

[61]  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)

[62]  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)

[63]  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)

[64]  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)

[65]  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)

[66]  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)

[67]  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)

[68]  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)

[69]  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)

[70]  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)

[71]  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)

[72]  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)

[73]  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)

[74]  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)

 

发明专利：

[1]      CN202410320481.8：一种基于脉搏波可视图互迁移学习的无袖带血压估计方法；发明人：张光磊，麻琛彬，刘振昌；申请人：北京航空航天大学。

[2]      CN202410221337.9：一种基于条件扩散模型的无创连续血压波形估计方法；发明人：张光磊，麻琛彬；申请人：北京航空航天大学。

[3]      CN202410158578.3：基于无监督脉搏波表征学习的无袖带血压估计方法及系统；发明人：张光磊，麻琛彬；申请人：北京航空航天大学。

[4]      CN202310045547.2：一种基于生成对抗网络的有创血压波形估计的方法及系统；发明人：张光磊，麻琛彬；申请人：北京航空航天大学。

[5]      CN202211609290.0：一种基于自监督迁移学习的无创血压估计方法；发明人：张光磊，麻琛彬，张皓南；申请人：北京航空航天大学。

[6]      CN202211608910.9：一种基于光电容积脉搏波的无创血压估计方法；发明人：张光磊，麻琛彬，张皓南；申请人：北京航空航天大学。

[7]      CN202210943352.5：多分支注意力先验参数化有限投影快速荧光断层重建方法；发明人：张光磊，张鹏，宋凡，麻琛彬，刘泽宇；申请人：北京航空航天大学。

[8]      CN202210280125.9：一种基于光电容积脉搏波实现有创血压波形估计的方法；发明人：张光磊，麻琛彬，孙洋洋，张鹏；申请人：北京航空航天大学。

[9]      CN202210027883.X：一种基于自注意力特征融合的胰腺癌病理图像分类方法；发明人：张光磊，冯云路，张天翊，赵雨，范广达，杨爱明，冯又丹，宋凡，张鹏；申请人：北京航空航天大学，中国医学科学院北京协和医院 。

[10]  CN202111484263.0：一种病理图像分类方法、设备、系统及存储介质；发明人：冯云路，张光磊，赵雨，杨爱明，吴东，范广达，张天翊；申请人：中国医学科学院北京协和医院，北京航空航天大学。

[11]  CN202111128553.1：基于多视角深度学习的非小细胞肺癌亚型分类系统；发明人：张光磊，宋凡，田哲源，范广达；申请人：北京志沅医疗科技有限公司。

[12]  CN202111090041.0：一种基于深度学习的胰腺癌病理图像分类方法及系统；发明人：张光磊，范广达，冯又丹，宋凡，张鹏；申请人：北京航空航天大学。

[13]  CN202110620901.0：基于自适应注意力机制的短时心电信号房颤自动检测系统；发明人：张光磊，张鹏，孙洋洋；申请人：北京航空航天大学。

[14]  CN202010817799.9：基于PPG的线性与非线性混合式无创连续血压测量系统；发明人：张光磊；申请人：北京航空航天大学。

[15]  CN202010794952.0：基于密集连接卷积循环神经网络的心电信号房颤检测装置；发明人：张光磊，李慧新；申请人：北京航空航天大学。

[16]  CN201911156777.6：基于PPG波形的混合式连续血压测量模型构建方法及系统；发明人：张光磊，荀琦峰，安子通，徐嘉豪，苏图克•伊不拉依木，孙洋洋；申请人：北京航空航天大学。

[17]  CN201911001658.3：基于一维密集连接卷积网络的心电信号房颤检测方法；发明人：张光磊，武新宇；申请人：北京航空航天大学。

[18]  CN201911001610.2：一种基于多尺度迁移学习的肺结节良恶性分类方法和系统；发明人：张光磊，李泽坤，范广达，邢彤彤；申请人：北京航空航天大学。

[19]  US8112147：Method and apparatus for generating determination indexes for identifying ECG interfering signals; YE Wenyu, HONG Junbiao, YUE Yu, ZHANG Guanglei (张光磊), SUN Zehui, ZOU Renqiang.

科研项目：

[1]      北京市自然科学基金面上项目，7242269，面向肺癌极早期检测的近红外II区荧光分子断层成像方法研究，2024/01 - 2025/12，在研，主持

[2]      国家重点研发计划，主动健康和人口老龄化科技应对，2023YFC3603500，无扰可穿戴生理多参数高鲁棒监测与动态分析关键技术研究，2023/12 - 2026/11，在研，课题负责人

[3]      国家重点研发计划，基础科研条件与重大科学仪器设备研发，2023YFF0715400，高时空分辨率光学和能谱显微CT双模态成像仪，2023/12 - 2026/11，在研，子课题负责人

[4]      国家自然科学基金面上项目，62271023，面向肺癌PD-L1分子三维可视化的超分辨率荧光分子断层成像方法研究，2023/01 - 2026/12，在研，主持

[5]      北京市科技创新券项目，基于人工智能的肺癌早期检测模型研究，2021/06 - 2021/09，已结题，主持

[6]      北京市自然科学基金面上项目，7202102，基于多尺度迁移学习的肺癌早期精准检测方法研究，2020/01 - 2021/12，已结题，主持

[7]      北航“青年拔尖人才计划”第七批，20181225，光学分子影像，2019/01 - 2022/12，已结题，主持

[8]      国家重点研发计划重点专项，2017YFA0700400，完整肝脏三维结构与功能信息的精准介观测量，2018/05 - 2023/04，已结题，参加

[9]      北航“医工百人计划”，20180329，光学分子影像新型成像方法研究，2018/01 - 2020/12，已结题，主持

[10]  中央高校基本科研业务费（人才基金），2017RC025，光学分子影像新型成像方法研究，2017/05 - 2018/03，已结题，主持

[11]  国家自然科学基金青年基金，61601019，基于XLCT/XCT双模态系统的深层原位肿瘤早期精准成像方法，2017/01 - 2019/12，已结题，主持

[12]  北京市自然科学基金青年基金，7164270，基于稀疏贝叶斯理论的X射线荧光断层成像方法研究，2016/01 - 2017/12，已结题，主持

[13]  中国博士后基金（一等），2015M570032，基于贝叶斯理论的X射线激发荧光断层成像方法研究，2015/04 - 2016/10，已结题，主持

[14]  中央高校基本科研业务费，2015JBM026，小动物X射线激发荧光断层成像方法研究，2015/01 - 2016/12，已结题，主持

[15]  中央高校基本科研业务费（人才基金），2017RC023，荧光参数反演成像性能优化方法研究，2017/07 - 2019/06，已结题，参加

[16]  国家自然科学基金面上项目，81571836，全无网格激发荧光断层成像方法研究，2016/01 - 2019/12，已结题，参加

[17]  国家自然科学基金青年基金，61401246，荧光分子动态参数反演成像方法研究，2015/01 - 2015/12，已结题，参加

[18]  国家自然科学基金面上项目，81271617，动态荧光分子断层成像的建模与重建方法研究，2013/01 - 2016/12，已结题，参加

[19]  国家重大科学仪器设备开发专项子课题，2011YQ030114，FMT系统核心器件与关键技术研发，2012/01-2016/12，已结题，参加

[20]  国家重点基础研究计划（973计划）子课题，2011CB707701，多模态分子影像成像理论和重建算法，2011/01-2015/12，已结题，参加

[21]  国家自然科学基金面上项目，81071191，基于空间编码并行激发的荧光分子断层成像方法的研究，2011/01 - 2013/12，已结题，参加

[22]  国家重点基础研究计划（973计划）子课题，2006CB705701，动态实时在体分子成像理论，技术和系统研究，2006/01-2010/12，已结题，参加

[23]  国家高技术研究发展计划（863计划）子课题，2006AA020803，核素与荧光双模小动物成像系统，2006/01-2010/12，已结题，参加

 

招生信息：

2025年博士后招聘方向：

1.       光学分子影像三维成像方法（深度学习重建算法、医学影像三维重建算法研究）

 

2025年博士生招生方向：

1.       光学分子影像三维成像方法（人工智能重建算法、医学影像三维重建算法研究）

 

2025年硕士生招生方向：

1.       医学影像人工智能分析方法（病理图像、放射影像等医学影像人工智能分析方法研究）

2.       医学智能可穿戴式诊疗设备（无创血压、无创血糖等AI算法及医疗可穿戴设备研究） 

2025年交流访问学生方向：

1.       医学影像人工智能分析方法（病理图像、放射影像等医学影像人工智能分析方法研究）

2.       医学智能可穿戴式诊疗设备（无创血压、无创血糖等AI算法及医疗可穿戴设备研究）

 

招生专业：

生物医学工程、计算机、数学、电子信息、自动化及相关专业。

• [1]. 2010.9 -- 2014.7

  清华大学       生物医学工程       博士研究生毕业       博士

• [2]. 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

• [3]. 2000.9 -- 2004.7

  西北工业大学       生物医学工程       大学本科毕业       学士

• [1]. 2023.8 -- 至今

  北京航空航天大学      生物与医学工程学院      副教授

• [2]. 2018.3 -- 2023.8

  北京航空航天大学      医工交叉创新研究院      “医工百人”特聘副研究员

• [3]. 2017.3 -- 2018.3

  北京交通大学      医学智能研究所      副所长      副教授

• [4]. 2015.10 -- 2016.10

  美国斯坦福大学（Stanford University）      博士后

• [5]. 2014.7 -- 2017.3

  北京交通大学      计算机与信息技术学院      博士后

• [6]. 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

[1].

IEEE Member

[2].

中国生物医学工程学会会员

[3].

生物医学光子学分会会员

[4].

医学物理分会青年委员

[5].

中国图象图形学会会员

[6].

中国光学工程学会高级会员

• [1] 光学分子影像三维成像方法（荧光分子断层成像、X射线激发荧光分子断层成像等）
• [2] 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）
• [3] 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）