ArticleNeuroImage2018
3D conditional generative adversarial networks for high-quality PET image estimation at low dose.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers, 2 of them syntheses that pooled it.
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Who cites it
145 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled it
- Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- MLPET: a localized neural network approach for probabilistic post-reconstruction PET image analysis using informed priors.EJNMMI physics · 2026Article
- Scan-wise generalized PET denoising with contrastive adversarial learning.Physics in medicine and biology · 2026Article
- Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data.Medical image analysis · 2026Article
- Accelerating imaging: deep learning for enhancedJapanese journal of radiology · 2026Article
- A Combinatorial Approach to Synthetic Data Generation for Machine Learning.SN computer science · 2026Article
- Toward Integrated Clinical-Computational Nuclear Medicine.PET clinics · 2026Review
- 3D Wasserstein Generative Adversarial Network with Dense U-Net-Based Discriminator for Preclinical fMRI Denoising.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Dual-branch residual encoder-decoder convolutional neural network (DB-REDCNN): a computed tomography-integrated multimodal network for positron emission tomography denoising.Quantitative imaging in medicine and surgery · 2025Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- DG-DiT: Dual-Branch Gating Diffusion Transformer for Multi-Tracer and Multi-Scanner Brain PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2025Article
- 2.5D Multi-View Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-Count PET Reconstruction With CT-Less Attenuation Correction.IEEE transactions on medical imaging · 2025Article
- Clinical validation of a deep learning model for low-count PET image enhancement.European journal of nuclear medicine and molecular imaging · 2025Article
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions.Medical review (2021) · 2025Review
- Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.European journal of nuclear medicine and molecular imaging · 2025Article
- Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.Medical & biological engineering & computing · 2025Article
- Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels.European journal of nuclear medicine and molecular imaging · 2025Article
85 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
Abstract
Positron emission tomography (PET) is a widely used imaging modality, providing insight into both the biochemical and physiological processes of human body. Usually, a full dose radioactive tracer is required to obtain high-quality PET images for clinical needs. This inevitably raises concerns about potential health hazards. On the other hand, dose reduction may cause the increased noise in the reconstructed PET images, which impacts the image quality to a certain extent. In this paper, in order to reduce the radiation exposure while maintaining the high quality of PET images, we propose a novel method based on 3D conditional generative adversarial networks (3D c-GANs) to estimate the high-quality full-dose PET images from low-dose ones. Generative adversarial networks (GANs) include a generator network and a discriminator network which are trained simultaneously with the goal of one beating the other. Similar to GANs, in the proposed 3D c-GANs, we condition the model on an input low-dose PET image and generate a corresponding output full-dose PET image. Specifically, to render the same underlying information between the low-dose and full-dose PET images, a 3D U-net-like deep architecture which can combine hierarchical features by using skip connection is designed as the generator network to synthesize the full-dose image. In order to guarantee the synthesized PET image to be close to the real one, we take into account of the estimation error loss in addition to the discriminator feedback to train the generator network. Furthermore, a concatenated 3D c-GANs based progressive refinement scheme is also proposed to further improve the quality of estimated images. Validation was done on a real human brain dataset including both the normal subjects and the subjects diagnosed as mild cognitive impairment (MCI). Experimental results show that our proposed 3D c-GANs method outperforms the benchmark methods and achieves much better performance than the state-of-the-art methods in both qualitative and quantitative measures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.