崔佳楠
时间:2024-04-27 21:07 来源: 作者: 点击:次
个人简介 浙江工业大学校聘副研究员，硕士生导师。2015年毕业于浙江大学获学士学位，2020年毕业于浙江大学获博士学位。2017年9月至2020年3月在美国哈佛医学院进行博士生联合培养研究。主要研究领域包括基于机器学习、深度学习的正电子发射断层扫描（PET）图像去噪、重建以及动脉自旋标记图像超分辨。相关工作累计发表文章22篇，其中以第一作者在EJNMMI，Medical Image Analysis等期刊发表SCI论文7篇，总被引350次。作为项目负责人主持国家自然科学基金青年基金1项、中国博士后科学基金面上项目1项，获得授权专利1项。2019年以结合人群信息和个人信息的无监督PET图像去噪方法获得了Fully 3D国际会议的Women in Imaging奖项。简历：2023.04-至今： 浙江工业大学，信息工程学院，校聘副研究员2020.12–2023.03：浙江大学，光电科学与工程学院，博士后 2015.09–2020.06：浙江大学，光电科学与工程学院，信息传感及仪器，博士 导师：刘华锋教授2017.03–2020.03：哈佛医学院，麻省总医院，联合培养 导师：Quanzheng Li 教授2011.09–2015.06：浙江大学，光电科学与工程学院，信息工程，学士 教学与课程 《脑与认知科学基础》 助教 科研项目 国家自然科学基金委员会, 青年科学基金项目，基于多模态数据的PET图像无监督去噪方法研究，2022.01-2024.12，在研,主持中国博士后科学基金会, 第69批面上资助二等，基于多尺度GAN网络的无监督动脉自旋标记图像超分辨研究，2021.06-2022.10，结题,主持之江实验室科研项目，多模态医学影像特征提取，2021.01-2023.12，在研,核心骨干国家自然科学基金委员会, 青年科学基金项目，基于双对比机制和三维容积采集的磁共振指纹式成像方法的研究，2018.01-2020.12，结题,参与深圳市科技计划项目，结构与示踪动力学驱动的PET图像重建，2018.03-2021.03，结题,参与 科研成果 期刊Cui, J., Gong, K.,Guo, N., …, Liu, H. and Li, Q., 2022. Unsupervised PET Logan Parametric Image Estimation using Conditional Deep Image Prior. Medical Image Analysis. 80,102519. Cui, J., Gong, K.,Guo, N., Wu, C., Meng, X., Kim, K., Zheng, K., Wu, Z., ..., Liu, H. and Li, Q., 2019. PET image denoising using unsupervised deep learning. European journal of nuclear medicine and molecular imaging, 46(13), pp.2780-2789. Cui, J., Gong, K.,Guo, N., …, Liu, H. and Li, Q., 2021. Populational and individual information based PET image denoising using conditional unsupervised learning. Physics in Medicine & Biology, 66(15), p.155001. Cui, J., Gong, K., Han, P., Liu, H., and Li, Q. 2022. Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network. Medical Physics, 49(4), 2373-2385. Cui, J., Yu, H., Chen, S., Chen, Y. and Liu, H., 2019. Simultaneous estimation and segmentation from projection data in dynamic PET. Medical physics, 46(3), pp.1245-1259.Cui, J., Qin, Z., Chen, S., Chen, Y. and Liu, H., 2019. Structure and Tracer Kinetics-Driven Dynamic PET Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences, 4(4), pp.400-409. Gong, K., Kim, K., Cui, J., Wu, D., and Li, Q. 2021. The evolution of image reconstruction in PET: From filtered back-projection to artificial intelligence. PET clinics, 16(4), 533-542. 会议Cui, J., Xie, Y., Joshi, A., …, Liu, H. and Li, Q., 2022, PET denoising and uncertainty estimation based on NVAE model using quantile regression loss. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 173-183). Springer, Cham. Cui, J., Xie, Y., Gong, K., …, Liu, H. and Li, Q. 2022. 2.5 D Nouveau VAE model for 11C-DASB PET image denoising and uncertainty estimation. Journal of Nuclear Medicine, 63(supplement 2), pp.3223-3223. Cui, J., Gong, K., Guo, N., …, Liu, H. and Li, Q. 2021. SURE-based Stopping Strategy for Fine-tunable Supervised PET Image Denoising. In 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (pp. 1-3). IEEE.Cui, J., Xie, Y., Gong, K., …, Liu, H. and Li, Q. 2021. PET denoising and uncertainty estimation based on NVAE model. In 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (pp. 1-3). IEEE.Cui, J., Gong, K., Han, P., Liu, H. and Li, Q., 2020, October. Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network. In International Workshop on Machine Learning in Medical Imaging (pp. 50-59). Springer, Cham.Cui, J., Gong, K.,Pan, T. and Li, Q., 2020. [68Ga]-DOTATATE PET Image Denoising using Unsupervised Deep Learning Can Improve CNR in A Wide Range. Journal of Nuclear Medicine, 61(supplement 1), pp.429-429. Cui, J., Gong, K., Guo, N., Wu, C., Kim, K., Liu, H. and Li, Q., 2019, May. Population and individual information guided PET image denoising using deep neural network. In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Vol. 11072, p. 110721E). International Society for Optics and Photonics.Cui, J., Gong, K., Guo, N., Kim, K., Liu, H. and Li, Q., 2019, March. CT-guided PET parametric image reconstruction using deep neural network without prior training data. In Medical Imaging 2019: Physics of Medical Imaging (Vol. 10948, p. 109480Z). International Society for Optics and Photonics. Cui, J., Gong, K., Guo, N., Meng, X., Kim, K., Liu, H. and Li, Q., 2018, November. CT-guided PET image denoising using deep neural network without prior training data. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) (pp. 1-3). IEEE.Xie, N., Gong, K., Guo, N., Qin, Z., Cui, J., Wu, Z., Liu, H. and Li, Q. 2020. Clinically translatable direct Patlak reconstruction from dynamic PET with motion correction using convolutional neural network. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 793-802). Springer, Cham. Zhou, Z., Guo, N., Cui, J., ... & Li, Q. 2019. Novel radiomic features based on graph theory for PET image analysis. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 1311-1314). IEEE.专利刘华锋，崔佳楠。名称：“一种基于张量字典约束的动态PET图像重建方法”授权专利号：201710287366.5 |