的个人主页 http://shi.buaa.edu.cn/zhang2018/zh_CN/index.htm
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] 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)
[2] 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.
[3] 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)
[4] 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)
[5] 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)
[6] 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)
[7] 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)
[8] 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)
[9] 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] 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.
[2] Z. Liu, T. Zhang, Y., and G. Zhang*, “Generating progressive images from pathological transitions via diffusion model,” MICCAI, 2024.
[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. (SCI)
[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, Q1, IF=8.5)
[5] 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, in press. (SCI, Q1, IF=3.8)
[6] 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)
[7] 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)
[8] 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)
[9] 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)
[10] 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)
[11] 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)
[12] 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.
[13] 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)
[14] 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.
[15] 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)
[16] 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)
[17] 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)
[18] 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)
[19] 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)
[20] 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)
[21] 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)
[22] 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)
[23] 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)
[24] 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)
[25] 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)
[26] 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)
[27] 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.
[28] 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)
[29] 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)
[30] 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)
[31] 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)
[32] 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)
[33] 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)
[34] 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)
[35] 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.
[36] 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)
[37] 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)
[38] 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)
[39] 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)
[40] 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)
[41] 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)
[42] 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)
[43] 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)
[44] 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)
[45] 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)
[46] 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)
[47] 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)
[48] 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)
[49] 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)
[50] 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)
[51] 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)
[52] 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)
[53] 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)
[54] 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)
[55] 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)
[56] 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)
[57] 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)
[58] 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)
[59] 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)
[60] 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)
[61] 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)
[62] 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)
[63] 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)
[64] 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)
[65] 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)
[66] 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)
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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。
清华大学  生物医学工程  博士研究生毕业  博士
西北工业大学  生物医学工程  硕士研究生毕业  硕士
西北工业大学  生物医学工程  大学本科毕业  学士
北京航空航天大学 生物与医学工程学院 “医工百人”特聘副研究员
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
北京交通大学 医学智能研究所 副所长 副教授
美国斯坦福大学(Stanford University) 博士后
北京交通大学 计算机与信息技术学院 博士后
深圳迈瑞生物医疗电子股份有限公司 资深工程师
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