ArticleEuropean journal of nuclear medicine and molecular imaging2019
PET image denoising using unsupervised deep learning.
Article in European journal of nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.
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Who cites it
75 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
- PET Image Reconstruction Using Deep Diffusion Image Prior.IEEE transactions on medical imaging · 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
- A Cross-modality Transformer Network for MR-guided Low-dose Tau PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging.Medical image analysis · 2026Article
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 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
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- 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
- 3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.European radiology · 2025Article
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
- IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement.European journal of nuclear medicine and molecular imaging · 2024Article
- Unsupervised speckle noise reduction technique for clinical ultrasound imaging.Ultrasonography (Seoul, Korea) · 2024Article
- Two-step optimization for accelerating deep image prior-based PET image reconstruction.Radiological physics and technology · 2024Article
- Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.EJNMMI physics · 2024Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising.IEEE transactions on medical imaging · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.IEEE transactions on radiation and plasma medical sciences · 2024Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
purposeImage quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsupervised deep learning, where no training pairs are needed.
methodsIn this method, the prior high-quality image from the patient was employed as the network input and the noisy PET image itself was treated as the training label. Constrained by the network structure and the prior image input, the network was trained to learn the intrinsic structure information from the noisy image and output a restored PET image. To validate the performance of the proposed method, a computer simulation study based on the BrainWeb phantom was first performed. A
resultsFor the simulation study, contrast recovery coefficient (CRC) vs. standard deviation (STD) curves showed that the proposed method achieved the best performance regarding the bias-variance tradeoff. For the clinical PET/CT dataset, the proposed method achieved the highest CNR improvement ratio (53.35% ± 21.78%), compared with the Gaussian (12.64% ± 6.15%, P = 0.002), NLM guided by CT (24.35% ± 16.30%, P = 0.002), BM4D (38.31% ± 20.26%, P = 0.002), and Deep Decoder (41.67% ± 22.28%, P = 0.002) methods. For the clinical PET/MR dataset, the CNR improvement ratio of the proposed method achieved 46.80% ± 25.23%, higher than the Gaussian (18.16% ± 10.02%, P < 0.0001), NLM guided by MR (25.36% ± 19.48%, P < 0.0001), BM4D (37.02% ± 21.38%, P < 0.0001), and Deep Decoder (30.03% ± 20.64%, P < 0.0001) methods. Restored images for all the datasets demonstrate that the proposed method can effectively smooth out the noise while recovering image details.
conclusionThe proposed unsupervised deep learning framework provides excellent image restoration effects, outperforming the Gaussian, NLM methods, BM4D, and Deep Decoder methods.
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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.