PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models

被引：0
|
作者：
Hein, Dennis [1 ,2 ]
Holmin, Staffan [3 ,4 ]
Szczykutowicz, Timothy [5 ]
Maltz, Jonathan S. [6 ]
Danielsson, Mats [1 ,2 ]
Wang, Ge [7 ]
Persson, Mats [1 ,2 ]
机构：
[1] The Department of Physics, KTH Royal Institute of Technology, Stockholm, Sweden
[2] MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
[3] The Department of Clinical Neuroscience, Karolinska Insitutet, Stockholm, Sweden
[4] The Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
[5] The Department of Radiology, University of Wisconsin, School of Medicine and Public Health, Madison,WI, United States
[6] GE HealthCare, United States
[7] The Department of Biomedical Engineering, School of Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy,NY, United States
来源：
arXiv | 2023年
关键词：
Engineering Village;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
CT Image - De-noising - Deep learning - Diffusion model - Generative model - Low-dose CT - Photon counting - Photon-counting CT - Poisson flow - Poisson flow generative model
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