Transforming a low-count nuclear medicine image into a high-count image using Dynamic Stochastic Resonance

被引:0
|
作者
Pandey, A. [1 ]
Kumar, S. [1 ]
Sharma, P. D. [1 ]
Patel, C. [1 ]
Kumar, R. [1 ]
机构
[1] All India Inst Med Sci, New Delhi, India
关键词
D O I
暂无
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
EP-0717
引用
收藏
页码:S715 / S716
页数:2
相关论文
共 50 条
  • [1] Validation of Dynamic Stochastic Resonance (DSR) for converting a low-count nuclear medicine (NM) image into a high-count image: a Phantom Study
    Pandey, A.
    Kumar, S.
    Chaudhary, J.
    Kumar, P.
    Sharma, P. D.
    Patel, C.
    Kumar, R.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2024, 51 : S753 - S754
  • [2] Low-count PET image restoration using sparse representation
    Li, Tao
    Jiang, Changhui
    Gao, Juan
    Yang, Yongfeng
    Liang, Dong
    Liu, Xin
    Zheng, Hairong
    Hu, Zhanli
    NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2018, 888 : 222 - 227
  • [3] Multimodal image registration for low-count SPECT images
    Backfrieder, W.
    Hatzl-Griesenhofer, M.
    Huber, H.
    Maschek, W.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2005, 32 : S76 - S76
  • [4] Virtual high-count PET image generation using a deep learning method
    Liu, Juan
    Ren, Sijin
    Wang, Rui
    Mirian, Niloufarsadat
    Tsai, Yu-Jung
    Kulon, Michal
    Pucar, Darko
    Chen, Ming-Kai
    Liu, Chi
    MEDICAL PHYSICS, 2022, 49 (09) : 5830 - 5840
  • [5] ON THE INVERSION OF THE ANSCOMBE TRANSFORMATION IN LOW-COUNT POISSON IMAGE DENOISING
    Makitalo, Markku
    Foi, Alessandro
    LNLA: 2009 INTERNATIONAL WORKSHOP ON LOCAL AND NON-LOCAL APPROXIMATION IN IMAGE PROCESSING, 2009, : 26 - 32
  • [6] Dynamic low-count PET image reconstruction using spatio-temporal primal dual network
    Hu, Rui
    Cui, Jianan
    Li, Chenxu
    Yu, Chengjin
    Chen, Yunmei
    Liu, Huafeng
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (13):
  • [7] Full-count PET recovery from low-count image using a dilated convolutional neural network
    Spuhler, Karl
    Serrano-Sosa, Mario
    Cattell, Renee
    DeLorenzo, Christine
    Huang, Chuan
    MEDICAL PHYSICS, 2020, 47 (10) : 4928 - 4938
  • [8] Deep Learning framework to synthesize High-Count Preclinical PET images from Low-Count Preclinical PET images
    Dutta, Kaushik
    Liu, Ziping
    Laforest, Richard
    Jha, Abhinav
    Shoghi, Kooresh, I
    MEDICAL IMAGING 2022: PHYSICS OF MEDICAL IMAGING, 2022, 12031
  • [9] Verification of image quality improvement of low-count bone scintigraphy using deep learning
    Murata, Taisuke
    Hashimoto, Takuma
    Onoguchi, Masahisa
    Shibutani, Takayuki
    Iimori, Takashi
    Sawada, Koichi
    Umezawa, Tetsuro
    Masuda, Yoshitada
    Uno, Takashi
    RADIOLOGICAL PHYSICS AND TECHNOLOGY, 2024, 17 (01) : 269 - 279
  • [10] Verification of image quality improvement of low-count bone scintigraphy using deep learning
    Taisuke Murata
    Takuma Hashimoto
    Masahisa Onoguchi
    Takayuki Shibutani
    Takashi Iimori
    Koichi Sawada
    Tetsuro Umezawa
    Yoshitada Masuda
    Takashi Uno
    Radiological Physics and Technology, 2024, 17 : 269 - 279