ArticleIEEE transactions on radiation and plasma medical sciences2021
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising.
Article in IEEE transactions on radiation and plasma medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline 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
- Scan-wise generalized PET denoising with contrastive adversarial learning.Physics in medicine and biology · 2026Article
- 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
- UR-cycleGAN: Denoising full-body low-dose PET images using cycle-consistent Generative Adversarial Networks.Journal of applied clinical medical physics · 2025Article
- POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.IEEE transactions on medical imaging · 2025Article
- MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer.Quantitative imaging in medicine and surgery · 2025Article
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
- Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation.Medical image analysis · 2024Article
- The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.Cancers · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks.Physics in medicine and biology · 2023Article
- FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.Medical image analysis · 2023Article
- An Investigation of Lesion Detection Accuracy for Artificial Intelligence-Based Denoising of Low-DoseJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction.Journal of digital imaging · 2023Article
- Inverse Design of Nanophotonic Devices Using Generative Adversarial Networks with the Sim-NN Model and Self-Attention Mechanism.Micromachines · 2023Article
- Federated Transfer Learning for Low-dose PET Denoising: A Pilot Study with Simulated Heterogeneous Data.IEEE transactions on radiation and plasma medical sciences · 2023Article
- A review on Deep Learning approaches for low-dose Computed Tomography restoration.Complex & intelligent systems · 2023Article
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8 authors.
Funding
Abstract
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than recently published state-of-the-art methods. We make our code available at https://github.com/90n9-yu/PT-WGAN.
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Registered trials
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