Related references
Note: Only part of the references are listed.Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance
Narges Aghakhan Olia et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2022)
Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging
Amirhossein Sanaat et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)
Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure
Yan-Ran (Joyce) Wang et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)
Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
Young Jin Jeong et al.
SCIENTIFIC REPORTS (2021)
LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks
Hengzhi Xue et al.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)
Artificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement
Juan Liu et al.
PET CLINICS (2021)
Deep learning-based image quality improvement of 18F-fluorodeoxyglucose positron emission tomography: a retrospective observational study
Junichi Tsuchiya et al.
EJNMMI PHYSICS (2021)
Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation
Amirhossein Sanaat et al.
MEDICAL PHYSICS (2021)
DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network
Zhanli Hu et al.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2021)
Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks
Zhanli Hu et al.
PHYSICS IN MEDICINE AND BIOLOGY (2020)
Supervised learning with cyclegan for low-dose FDG PET image denoising
Long Zhou et al.
MEDICAL IMAGE ANALYSIS (2020)
Study of low-dose PET image recovery using supervised learning with CycleGAN
Kui Zhao et al.
PLOS ONE (2020)
Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers
Masafumi Kidoh et al.
MAGNETIC RESONANCE IN MEDICAL SCIENCES (2020)
Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease
Andreas Hauptmann et al.
MAGNETIC RESONANCE IN MEDICINE (2019)
Deep learning-based image restoration algorithm for coronary CT angiography
Fuminari Tatsugami et al.
EUROPEAN RADIOLOGY (2019)
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
Motonori Akagi et al.
EUROPEAN RADIOLOGY (2019)
Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss
Jiahong Ouyang et al.
MEDICAL PHYSICS (2019)
Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study
Sydney Kaplan et al.
JOURNAL OF DIGITAL IMAGING (2019)
Cycle-consistent adversarial denoising network for multiphase coronary CT angiography
Eunhee Kang et al.
MEDICAL PHYSICS (2019)
PET Image Denoising Using a Deep Neural Network Through Fine Tuning
Kuang Gong et al.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2019)
Generative Adversarial Networks for Noise Reduction in Low-Dose CT
Jelmer M. Wolterink et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2017)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
A Perspective on Deep Imaging
Ge Wang
IEEE ACCESS (2016)
Quantitative imaging of protein targets in the human brain with PET
Roger N. Gunn et al.
PHYSICS IN MEDICINE AND BIOLOGY (2015)
Non-Local Means Denoising of Dynamic PET Images
Joyita Duna et al.
PLOS ONE (2013)
Cardiac positron emission tomography imaging
J Machac
SEMINARS IN NUCLEAR MEDICINE (2005)
Image quality assessment: From error visibility to structural similarity
Z Wang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2004)