崔佳楠

• 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、重建以及基于大模型的脑肿瘤分割等。相关工作累计发表文章37篇，其中以一作或通讯发表在IEEE TMI、MedIA、EJNMMI和MICCAI等顶级期刊/会议上共22篇，正面被引642次，单篇最高引用量达330次；授权专利4项。获浙江省科协青年人才托举培养项目（2025），浙江省技术发明二等奖，中国图象图形学学会科技进步二等奖，本领域顶级国际会议Fully 3D的Women in Imaging奖项。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，参与国家自然科学基金联合重点项目、浙江省科技厅“尖兵领雁”等项目多项。

• 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

《面向对象C++编程A》 

《程序设计基础C》

《人工智能原理及应用A》  

《计算机视觉理论与方法》 博士生课

科研项目：

• 浙江省科学技术协会, 浙江省科协青年人才托举培养项目, 2025.01-2027.12, 在研, 主持

• 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，结题,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

• 国家自然科学基金委员会, 联合基金项目, 磁共振引导的脑部恶性肿瘤分子影像实时精准诊疗研究, 2024.01-2027.12, 在研, 参与

• 浙江省科技厅, “尖兵领雁+X”科技计划, 新一代智能医疗大数据与多模态知识融合关键技术研究, 2025.01-2026.12, 在研, 参与

• 之江实验室科研项目，多模态医学影像特征提取，2021.01-2023.12，结题,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

科研奖励：

• 2024年度浙江省技术发明二等奖：高灵敏PET/CT成像技术及应用，排名5/6

• 2025年度中国图象图形学学会科技进步二等奖：超低剂量PET/CT成像关键技术及应用，排名4/6

• Women in Imaging Award，第15届国际放射医学与核医学全三维图像重建会议（Fully 3D Conference），2019，1/1

期刊（*共一，#通讯）

• Cui, J., Wu, J., Wu, Z., He, J., Zeng, Q., Chen, Z. and Feng, Y., 2025. Deep Residual Compensation Model for Unsupervised PET Partial Volume Correction. IEEE Transactions on Medical Imaging., Early Access

• Luo, Y.*, Cui, J.*, Zhang, H., Yang, Y., Shalihaer, H., Feng, Y., Su, X., Liu, H. and Li, X., 2026. Generalizable Unpaired PET Image Enhancement via Two-Stage Diffusion Framework. IEEE Transactions on Radiation and Plasma Medical Sciences.  Early Access

• Ran, H., Cui, J., Feng, X., Ye, Y., Jin, Y., Chen, Y., Zhao, B., Hu, R., Guo, M., Su, X. and Liu, H., 2025. Reinforced physiology-informed learning for image completion from partial-frame dynamic PET imaging. Medical Image Analysis, p.103767.

• Cui, J., Luo, Y., Chen, D., Shi, K., Su, X. and Liu, H., 2024. IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement. European journal of nuclear medicine and molecular imaging, 51(13), pp.3874-3887.

• Cui, J., Xie, Y., Guo, N., Feng, Y. and Li, Q., 2024. Pet image denoising using consistent denoising diffusion model. Journal of Nuclear Medicine，65 (supplement 2), 241799-241799

• 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. 

  
  

会议

• Wu, Z., Wu, J., Cui, J.#, Feng, Y. and Chen, Z.#, 2025, September. Bowsher Prior Enhanced Unsupervised PET Image Denoising. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 88-97). Cham: Springer Nature Switzerland.

• Wu, J., Wu, Z., He, J., Zeng, Q., Chen, Z., Feng, Y.# and Cui, J.#, 2025, April. PET Partial Volume Correction Based on Unsupervised Deep Residual Compensation Model. In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.

• 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

• 崔佳楠; 吴建凯; 基于条件深度残差补偿的PET图像部分容积校正方法,授权专利号：ZL202411837131.5

• 崔佳楠; 叶慧慧; 涂文靖; 刘华锋; 基于多通道下采样的无监督磁共振图像超分辨方法及装置,授权专利号：ZL202211698781.7

• 刘华锋; 罗仪; 崔佳楠; 一种基于CycleGAN的非配对PET图像质量增强方法, 授权专利号：ZL202310724652.9

• 刘华锋; 崔佳楠; 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：ZL201710287366.5

中国移动杯·第一届浙江省大学生人工智能竞赛一等奖（指导教师）, 浙江省大学生科技竞赛委员会, 2025(崔佳楠; 何建忠)

• 国际电气电子工程师学会（IEEE）会员

• 中国图象图形学学会医学影像专委会委员

• 企业联络与标准化工作委员会委员

• 受邀担任 Applied Science 特刊的客座编辑

• Fully3D 2019 Poster Forward Session 主席(Chair)

• 担任包括 IEEE TMI、 IEEE TRPMS、 JBHI 等多家国际期刊的审稿人