最终学位：博士
导师类型：
电子邮箱：pengzhang@sxu.edu.cn
联系电话：0351-7010566
研究方向：光学分子影像与人工智能
张鹏，博士，研究方向为光学分子影像与人工智能，聚焦于荧光分子断层成像的三维重建算法及医学影像人工智能分析方法研究。2023年6月毕业于北京航空航天大学，获得博士学位。近年来，以第一作者在IEEE Trans. Med. Imag., IEEE. Trans. Comput. Imag., Comput. Methods. Programs. Biomed., Comput. Biol. Med., Biomed. Signal Process. Control., Biomed. Opt. Express., Phys. Med. Biol., J. Biomed. Opt., 等期刊上累计发表10篇SCI论文。第一作者发表EI会议论文3篇；发明专利2项。IEEE Transactions on image Processing (IEEE-TIP), Physics in Medicine and Biology (PMB), Biomedical Optics Express (BOE)等国际期刊的审稿人。
曾独立主持1项山西省创新创业项目（2018），1项北航博士研究生卓越学术基金（2022）。曾参与科技部重点研发计划（2016YFC0101600），国家自然科学基金面上等多个项目。曾获得过2项国家级团体竞赛奖项（1项全国二等奖《“兆易创新杯”第十四届中国研究生电子设计竞赛》，1项全国三等奖《第十六届“挑战杯”全国大学生课外学术科技作品竞赛》）；3项省部级团体竞赛奖项（1项金奖《第五届山西省“互联网+” 大学生创新创业大赛》，1项特等奖《山西省第十六届“兴晋挑战杯”大学生课外学术科技作品竞赛》，1项一等奖《“兆易创新杯”第十四届中国研究生电子设计竞赛华北分赛区》）。曾获得2023年北京市优秀毕业生，北京航空航天大学“十佳研究生”（全校仅10名）等多项荣誉。
欢迎具有计算机、生物医学工程、数学、电子信息等相关专业背景且对从事深度学习和医学影像交叉研究感兴趣的本科生及硕士生与我联系。
[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, 40(11): 3217–3228. (SCI, Q1, IF= 10.6)
[2] P. Zhang, C. Ma, F. Song, Z. Liu, H. Wu, Y. Feng, Y. He, D. Wang, and G. Zhang, SVRNet: First Investigation of Single-view Reconstruction Network for Fluorescence Molecular Tomography, IEEE. Trans. Comput. Imag., In Press, 2023. (SCI, Q2, IF= 5.4)
[3] P. Zhang, C. Ma, F. Song, Y. Sun, Y. Feng, Y. He, T. Zhang, and G. Zhang, D2-RecST: Dual-domain Joint Reconstruction Strategy for Fluorescence Molecular Tomography Based on Image Domain and Perception Domain, Comput. Methods. Programs. Biomed., 2022: 107293. (SCI, Q2, IF=6.1)
[4] 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, Q2, IF=7.7)
[5] P. Zhang, C. Ma, F. Song, Y. Sun, G. Fan, T. Zhang, Y. Feng, Y. He, 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 =3.8)
[6] P. Zhang, Z. Gui, G. Guo, and Y. Shang, Approaches to denoise the diffuse optical signals for tissue blood flow measurement,” Biomed. Opt. Express., 9, 6170-6185, 2018. (SCI, Q2, IF =3.8)
[7] 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(10): 10TR01. (SCI, Q2, IF=3.5)
[8] P. Zhang, F. Song, C. Ma, Z. Liu, 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. (SCI, Q2, IF=3.5)
[9] P. Zhang, C. Ma, F. Song, Y. Sun, Y. Feng, Y. He, T. Zhang, and G. Zhang, 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).
[10] F. Liu#, P. Zhang# (共一), Z. Liu, F. Song, C. Ma, Y. Sun, Y. Feng, Y. He, G. Zhang, In vivo detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography, J. Biomed. Opt., 2022, 27(7): 070501. (SCI, Q2, IF =3.5).
[1] 北京航空航天大学博士研究生卓越学术基金：基于深度学习的荧光分子断层成像三维重建方法研究，主持人，2022.5-2023.5，已结题
[2] 山西省研究生教育创新项目：近红外漫射光血流测量的N阶线性算法的去噪方法研究，主持人，2018.9-2019.6，已结题
[3] 国家自然科学基金（面上项目），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，参与人，2023/01–2026/12
[4] 国家重点研发计划(2016YFC0101600)，结合形态学影像的近红外漫射光血流断层成像(DCS/DCT)系统，参与人，2016/07-2018/12