Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning

• URL: http://arxiv.org/abs/2604.16925v1
• Date: Sat, 18 Apr 2026 09:16:38 GMT
• Title: Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning
• Abstract summary: Cross-dose denoising for low-dose positron emission tomography (LDPET) has been proposed to address the limited generalization of models trained at a single noise level.<n>We propose a unified residual noise learning framework that estimates noise directly from low-dose PET images rather than predicting full-dose images.
• Score: 4.0226843660257225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-dose denoising for low-dose positron emission tomography (LDPET) has been proposed to address the limited generalization of models trained at a single noise level. In practice, neural networks trained on a specific dose level often fail to generalize to other dose conditions due to variations in noise magnitude and statistical properties. Conventional "one-size-for-all" models attempt to handle this variability but tend to learn averaged representations across noise levels, resulting in degraded performance. In this work, we analyze this limitation and show that standard training formulations implicitly optimize an expectation over heterogeneous noise distributions. To this end, we propose a unified residual noise learning framework that estimates noise directly from low-dose PET images rather than predicting full-dose images. Experiments on large-scale multi-dose PET datasets from two medical centers demonstrate that the proposed method outperforms the "one-size-for-all" model, individual dose-specific U-Net models, and dose-conditioned approaches, achieving improved denoising performance. These results indicate that residual noise learning effectively mitigates the averaging effect and enhances generalization for cross-dose PET denoising.

Related papers

• NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training [81.58321000203712]
  NoiseRater is a meta-learning framework for instance-level noise valuation in diffusion model training.<n>NoiseRater assigns importance scores to individual noise realizations conditioned on data and timestep.<n>Experiments on FFHQ and ImageNet demonstrate that not all noise samples contribute equally.
  arXiv  Detail & Related papers  (2026-05-02T19:43:16Z)
• Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs [13.741630476895773]
  This paper provides the first convergence analysis for training transformer-based diffusion models.<n>We consider the population Denoising Diffusion Probabilistic Model (DDPM) objective for denoising data.<n>A deeper investigation reveals that trained a mean denoising module enables the trained model to approximate the oracle Minimum Mean Squared Error (MMSE) of the injected noise in the diffusion steps.
  arXiv  Detail & Related papers  (2026-04-11T07:46:15Z)
• Noise Conditional Variational Score Distillation [60.38982038894823]
  Noise Conditional Variational Score Distillation (NCVSD) is a novel method for distilling pretrained diffusion models into generative denoisers.<n>By integrating this insight into the Variational Score Distillation framework, we enable scalable learning of generative denoisers.
  arXiv  Detail & Related papers  (2025-06-11T06:01:39Z)
• Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising [16.937074760667745]
  Low-count positron emission tomography (LCPET) imaging can reduce patients' exposure to radiation but often suffers from increased image noise and reduced lesion detectability.<n> Diffusion models have shown promise in LCPET denoising for recovering degraded image quality.<n>We propose a novel noise-embedded federated learning diffusion model (Fed-NDIF) to address these challenges.
  arXiv  Detail & Related papers  (2025-03-20T18:37:46Z)
• Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet [3.83243615095535]
  Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings. We propose a novel 3D ControlNet-based denoising method for whole-body PET imaging.
  arXiv  Detail & Related papers  (2024-11-08T03:06:47Z)
• Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising [13.694516702501097]
  PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise levels in PET imaging. Our Conditional Score-based Residual Diffusion (CSRD) model addresses these issues by incorporating a refined score function and 3D patch-wise training strategy.
  arXiv  Detail & Related papers  (2024-09-30T19:35:22Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• NLIP: Noise-robust Language-Image Pre-training [95.13287735264937]
  We propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. Our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way.
  arXiv  Detail & Related papers  (2022-12-14T08:19:30Z)
• Enhancing convolutional neural network generalizability via low-rank weight approximation [6.763245393373041]
  Sufficient denoising is often an important first step for image processing. Deep neural networks (DNNs) have been widely used for image denoising. We introduce a new self-supervised framework for image denoising based on the Tucker low-rank tensor approximation.
  arXiv  Detail & Related papers  (2022-09-26T14:11:05Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.