# Unified Noise-aware Network for Low-count PET Denoising

arXiv: https://arxiv.org/abs/2304.14900 · HTML: https://mlsanity.com/paper/2304.14900/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2023.

## Built on by (most cited first, 10 of 10)

- [DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging](https://mlsanity.com/paper/2311.04248/) (2023)
- [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)
- [POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation](https://mlsanity.com/paper/2401.14285/) (2024)
- [Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising](https://mlsanity.com/paper/2503.16635/) (2025)
- [M2Diff: Multi-Modality Multi-Task Enhanced Diffusion Model for MRI-Guided Low-Dose PET Enhancement](https://mlsanity.com/paper/2603.09075/) (2026)
- [Dual Prompting for Diverse Count-level PET Denoising](https://mlsanity.com/paper/2505.03037/) (2025)
- [MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising](https://mlsanity.com/paper/2505.05112/) (2025)
- [Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging](https://mlsanity.com/paper/2508.14364/) (2025)
- [Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning](https://mlsanity.com/paper/2604.16925/) (2026)
- [HDDPM: Heteroscedastic Denoising Diffusion Probabilistic Model for Quantitative Low-Count Brain PET Recovery](https://mlsanity.com/paper/2606.28513/) (2026)

## Abstract (arXiv)

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.