# Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model arXiv: https://arxiv.org/abs/2308.13072 ยท HTML: https://mlsanity.com/paper/2308.13072/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 19 of its 51 references that are in the map. - First posted on arXiv 24 Aug 2023. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 19 of 19) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [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) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021) - [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023) - [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017) - [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022) - [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022) - [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023) - [PET image denoising based on denoising diffusion probabilistic models](https://mlsanity.com/paper/2209.06167/) (2022) - [Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT](https://mlsanity.com/paper/2304.04920/) (2023) - [PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos](https://mlsanity.com/paper/2103.09009/) (2021) - [High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition](https://mlsanity.com/paper/2307.05541/) (2023) - [Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models](https://mlsanity.com/paper/2209.14467/) (2022) ## Built on by (most cited first, 15 of 15) - [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) - [PET Image Reconstruction Using Deep Diffusion Image Prior](https://mlsanity.com/paper/2507.15078/) (2025) - [T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioning](https://mlsanity.com/paper/2409.01622/) (2024) - [Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet](https://mlsanity.com/paper/2411.05302/) (2024) - [M2Diff: Multi-Modality Multi-Task Enhanced Diffusion Model for MRI-Guided Low-Dose PET Enhancement](https://mlsanity.com/paper/2603.09075/) (2026) - [PPDM: Pixel Puzzling Diffusion Model for Speed and Memory Efficient Volumetric Medical Image Translation](https://mlsanity.com/paper/2606.15323/) (2026) - [Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data](https://mlsanity.com/paper/2410.23628/) (2024) - [Exploiting network optimization stability for enhanced PET image denoising using deep image prior](https://mlsanity.com/paper/2502.11259/) (2025) - [Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging](https://mlsanity.com/paper/2508.14364/) (2025) - [Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction](https://mlsanity.com/paper/2509.00395/) (2025) - [Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior](https://mlsanity.com/paper/2512.19584/) (2025) - [Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction](https://mlsanity.com/paper/2512.22237/) (2025) - [Text controllable PET denoising](https://mlsanity.com/paper/2601.20990/) (2026) - [VLM- and LLM-Driven Multi-Agent System for PET Image Denoising](https://mlsanity.com/paper/2608.13791/) (2026) - [Flow Matching-Based PET Image Reconstruction](https://mlsanity.com/paper/2608.20112/) (2026) ## Abstract (arXiv) Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. Approach: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Results: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278+/-0.122%, PSNR of 33.783+/-0.824dB, SSIM of 0.964+/-0.009, NCC of 0.968+/-0.011, HRS of 4.543, and SUV Error of 0.255+/-0.318%, with an average generation time of 62 seconds per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12x faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973+/-0.066%, PSNR of 36.172+/-0.801dB, SSIM of 0.984+/-0.004, NCC of 0.990+/-0.005, HRS of 4.428, and SUV Error of 0.151+/-0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario.