# PET image denoising based on denoising diffusion probabilistic models

arXiv: https://arxiv.org/abs/2209.06167 · HTML: https://mlsanity.com/paper/2209.06167/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction](https://mlsanity.com/paper/2112.05146/) (2021)
- [Robust Compressed Sensing MRI with Deep Generative Priors](https://mlsanity.com/paper/2108.01368/) (2021)
- [Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data](https://mlsanity.com/paper/2107.09892/) (2021)
- [Using Uncertainty in Deep Learning Reconstruction for Cone-Beam CT of the Brain](https://mlsanity.com/paper/2108.09229/) (2021)

## Built on by (most cited first, 30 of 57)

- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models](https://mlsanity.com/paper/2402.05210/) (2024)
- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation](https://mlsanity.com/paper/2405.14802/) (2024)
- [Score-Based Generative Models for PET Image Reconstruction](https://mlsanity.com/paper/2308.14190/) (2023)
- [Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising](https://mlsanity.com/paper/2308.04682/) (2023)
- [Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model](https://mlsanity.com/paper/2308.13072/) (2023)
- [2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction](https://mlsanity.com/paper/2406.08374/) (2024)
- [Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data](https://mlsanity.com/paper/2510.14831/) (2025)
- [DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging](https://mlsanity.com/paper/2311.04248/) (2023)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation](https://mlsanity.com/paper/2405.12223/) (2024)
- [Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction](https://mlsanity.com/paper/2412.04339/) (2024)
- [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025)
- [RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration](https://mlsanity.com/paper/2404.16017/) (2024)
- [PET Image Reconstruction Using Deep Diffusion Image Prior](https://mlsanity.com/paper/2507.15078/) (2025)
- [Supervise-assisted Multi-modality Fusion Diffusion Model for PET Restoration](https://mlsanity.com/paper/2602.11545/) (2026)
- [Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data](https://mlsanity.com/paper/2405.12996/) (2024)
- [Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction](https://mlsanity.com/paper/2407.00944/) (2024)
- [ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schr"odinger Bridge](https://mlsanity.com/paper/2311.14542/) (2023)
- [RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models](https://mlsanity.com/paper/2410.00307/) (2024)
- [FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation](https://mlsanity.com/paper/2408.02075/) (2024)
- [Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI](https://mlsanity.com/paper/2507.19186/) (2025)
- [TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2512.14550/) (2025)
- [MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction](https://mlsanity.com/paper/2406.13150/) (2024)
- [Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising](https://mlsanity.com/paper/2410.00184/) (2024)
- [Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet](https://mlsanity.com/paper/2411.05302/) (2024)
- [Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling](https://mlsanity.com/paper/2412.12532/) (2024)
- [PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts](https://mlsanity.com/paper/2502.21260/) (2025)
- [Whole-body PET image denoising for reduced acquisition time](https://mlsanity.com/paper/2303.16085/) (2023)

## Abstract (arXiv)

Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic models (DDPM) are distribution learning-based models, which try 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. Under the DDPM framework, one way to perform PET image denoising is to provide the PET image and/or the prior image as the network input. Another way is to supply the prior image as the input with the PET image included in the refinement steps, which can fit for scenarios of different noise levels. 120 18F-FDG datasets and 140 18F-MK-6240 datasets were utilized to evaluate the proposed DDPM-based methods. Quantification show that the DDPM-based frameworks with PET information included can generate better results than the nonlocal mean and Unet-based denoising methods. Adding additional MR prior in the model can help achieve better performance and further reduce the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information can result in large bias. Regional and surface quantification shows that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference can achieve the best performance. In summary, DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean and Unet-based denoising methods.