ArticlePhysics in medicine and biology2019
Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 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
- Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.European journal of nuclear medicine and molecular imaging · 2026Article
- 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
- UR-cycleGAN: Denoising full-body low-dose PET images using cycle-consistent Generative Adversarial Networks.Journal of applied clinical medical physics · 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
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI.European journal of nuclear medicine and molecular imaging · 2025Article
- Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.Biomedical engineering online · 2025Article
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
- IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement.European journal of nuclear medicine and molecular imaging · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- Intelligent ultrafast total-body PET for sedation-free pediatric [European journal of nuclear medicine and molecular 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
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- LowQuantitative imaging in medicine and surgery · 2024Article
- Whole-body PET image denoising for reduced acquisition time.Frontiers in medicine · 2024Article
- SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging.Frontiers in nuclear medicine · 2024Article
- PET image denoising based on denoising diffusion probabilistic model.European journal of nuclear medicine and molecular imaging · 2024Article
- Feasibility of a deep learning algorithm to achieve the low-doseThe British journal of radiology · 2023Article
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9 authors.
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Abstract
Lowering either the administered activity or scan time is desirable in PET imaging as it decreases the patient's radiation burden or improves patient comfort and reduces motion artifacts. But reducing these parameters lowers overall photon counts and increases noise, adversely impacting image contrast and quantification. To address this low count statistics problem, we propose a cycle-consistent generative adversarial network (Cycle GAN) model to estimate diagnostic quality PET images using low count data. Cycle GAN learns a transformation to synthesize diagnostic PET images using low count data that would be indistinguishable from our standard clinical protocol. The algorithm also learns an inverse transformation such that cycle low count PET data (inverse of synthetic estimate) generated from synthetic full count PET is close to the true low count PET. We introduced residual blocks into the generator to catch the differences between low count and full count PET in the training dataset and better handle noise. The average mean error and normalized mean square error in whole body were -0.14% ± 1.43% and 0.52% ± 0.19% with Cycle GAN model, compared to 5.59% ± 2.11% and 3.51% ± 4.14% on the original low count PET images. Normalized cross-correlation is improved from 0.970 to 0.996, and peak signal-to-noise ratio is increased from 39.4 dB to 46.0 dB with proposed method. We developed a deep learning-based approach to accurately estimate diagnostic quality PET datasets from one eighth of photon counts, and has great potential to improve low count PET image quality to the level of diagnostic PET used in clinical settings.
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