# Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet

arXiv: https://arxiv.org/abs/2411.05302 · HTML: https://mlsanity.com/paper/2411.05302/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [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)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [PET image denoising based on denoising diffusion probabilistic models](https://mlsanity.com/paper/2209.06167/) (2022)
- [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)
- [DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging](https://mlsanity.com/paper/2311.04248/) (2023)

## Built on by (most cited first, 2 of 2)

- [DEMIST: Decoupled Multi-stream latent diffusion for Quantitative Myelin Map Synthesis](https://mlsanity.com/paper/2511.12396/) (2025)
- [3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising](https://mlsanity.com/paper/2601.07093/) (2026)

## Abstract (arXiv)

Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings, influenced by factors such as scanner types, tracer choices, dose levels, and acquisition times. In this work, we proposed a novel 3D ControlNet-based denoising method for whole-body PET imaging. We first pre-trained a 3D Denoising Diffusion Probabilistic Model (DDPM) using a large dataset of high-quality normal-dose PET images. Following this, we fine-tuned the model on a smaller set of paired low- and normal-dose PET images, integrating low-dose inputs through a 3D ControlNet architecture, thereby making the model adaptable to denoising tasks in diverse clinical settings. Experimental results based on clinical PET datasets show that the proposed framework outperformed other state-of-the-art PET image denoising methods both in visual quality and quantitative metrics. This plug-and-play approach allows large diffusion models to be fine-tuned and adapted to PET images from diverse acquisition protocols.