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

《脑与认知科学基础》 助教 

• 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

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