ArticleEuropean journal of nuclear medicine and molecular imaging2024
PET image denoising based on denoising diffusion probabilistic model.
Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U‑Net and Diffusion Models.Chemical & biomedical imaging · 2026Article
- PET Image Reconstruction Using Deep Diffusion Image Prior.IEEE transactions on medical imaging · 2026Article
- LDM-Morph: Latent diffusion model guided deformable image registration.Pattern recognition · 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
- Automated measurement of the minimum macular hole diameter based on optical coherence tomography images.BMC ophthalmology · 2026Article
- A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.EJNMMI physics · 2026Article
- Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.EJNMMI physics · 2026Article
- CT-to-PET Synthesis in the Head-Neck and Thoracic Region via Conditional 3D Latent Diffusion Modeling.Bioengineering (Basel, Switzerland) · 2026Article
- Accelerating imaging: deep learning for enhancedJapanese journal of radiology · 2026Article
- Total-body [European journal of nuclear medicine and molecular imaging · 2026Article
- LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising.IEEE transactions on medical imaging · 2026Article
- RadGazeGen: radiomics and gaze-guided chest X-ray generation using diffusion models.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Iterative learning for joint image denoising and motion artifact correction of 3D brain MRI.Medical image analysis · 2026Article
- SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.EJNMMI physics · 2026Article
- A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.Frontiers in artificial intelligence · 2026Article
- Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging.Medical image analysis · 2026Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
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5 authors.
Funding
Abstract
purposeDue to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising.
methodsUnder the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [[Formula: see text]F]FDG datasets and 140 brain [[Formula: see text]F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods.
resultsQuantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance.
conclusionDDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.
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