AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising

Chao Li; Renyiming Li; Ruili Li; Xuepeng Liu; Yan Li; Yin Dai; YueYang Teng; Zetong Liu

REVIEW 2 major objections 2 minor 26 references

AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising

T0 review · 2 major / 2 minor · reviewed 2026-07-02 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read An anatomy- and frequency-guided diffusion model recovers high-dose PET images from low-dose inputs using CT conditioning.

desk verdict AnF-DiffPET adds three named modules to a CT-conditioned diffusion denoiser for low-dose PET, but the abstract gives no ablations so we cannot tell what actually drives the gains. read the letter →

arxiv 2607.00509 v1 pith:4VBCBFSV submitted 2026-07-01 cs.CV

Xuepeng Liu , Ruili Li , Zetong Liu , Renyiming Li , Yan Li , Yin Dai , Chao Li , Yueyang Teng This is my paper

classification cs.CV

keywords PETdenoisingdiffusionmodelsCTconditioninganatomicalguidancefrequencydomainlow-doseimagingmedicalimagereconstructiontransformer

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces AnF-DiffPET, a diffusion framework that denoises low-dose PET scans by conditioning on CT images and adding targeted guidance in both anatomical structure and frequency content. It tackles three specific difficulties: weak anatomical cues during denoising, unstable multi-scale feature handling, and inconsistent frequency-domain recovery of tracer uptake. The approach combines three modules to modulate features and enforce consistency, then demonstrates gains in visual quality, alignment with CT anatomy, and measured uptake accuracy across four separate PET/CT collections when compared with CNN, GAN, transformer, and other diffusion baselines.

What carries the argument

Anatomical-Frequency Guidance (AFG) that modulates diffusion features using both CT-derived anatomical structure and frequency-domain information to steer the denoising trajectory.

What would settle it

An ablation experiment that removes AFG, MSCTR, and FCHM one at a time and finds no measurable drop in PSNR, SSIM, or SUV error on the same test sets would falsify the claim that these modules are the primary source of improvement.

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Extended reading notes

Core claim

The AnF-DiffPET framework integrates Anatomical-Frequency Guidance (AFG), Multi-Scale Cross-Transformer Reconstruction (MSCTR), and Frequency-Contrastive Hard Mining (FCHM) into a CT-conditioned diffusion process to recover high-dose PET images from low-dose inputs, achieving improved fidelity, anatomical consistency, and quantitative accuracy.

Load-bearing premise

The reported gains are driven mainly by the three added modules rather than by the underlying diffusion training procedure or by tuning choices specific to the four evaluation datasets.

Editorial extensions

If this is right

Low-dose PET acquisitions can retain higher anatomical fidelity and more reliable tracer uptake values than with prior CNN or GAN denoisers.
CT conditioning becomes a practical source of anatomical and frequency guidance inside diffusion-based medical image restoration.
Frequency-domain contrastive mining reduces artifacts that standard diffusion models produce when recovering fine uptake patterns.
Multi-scale cross-transformer blocks stabilize feature propagation across the denoising steps at different resolutions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same guidance pattern could be tested on SPECT or MRI denoising where paired anatomical images are available.
If the modules prove robust, clinical protocols could lower injected activity further while preserving diagnostic quantification.
The frequency-contrastive component might generalize to other inverse problems that suffer from band-limited noise.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The paper's core idea is a diffusion framework for turning low-dose PET into high-dose equivalents while using CT for conditioning. It introduces Anatomical-Frequency Guidance to steer features, a Multi-Scale Cross-Transformer for reconstruction, and Frequency-Contrastive Hard Mining to focus on tricky frequency components. The abstract reports better fidelity, anatomical consistency, and quantitative metrics than CNN, GAN, transformer, and prior diffusion baselines across four datasets.

What stands out is the explicit attempt to handle anatomy and frequency issues that standard diffusion models often ignore in PET. Releasing code and models is useful for anyone who wants to test the approach.

The main weakness is exactly the one flagged in the stress-test note. No ablation tables or module-removal results appear in the provided abstract, so the improvements could come from the diffusion training schedule, the CT conditioning itself, or dataset-specific choices rather than the three proposed modules. Without error bars, statistical tests, or parameter counts, it is impossible to judge whether the gains are reliable or reproducible. The circularity risk is standard for this kind of empirical work.

This paper is aimed at groups already working on diffusion models for medical image denoising or low-dose PET reconstruction. A reader in that narrow area might pick up the module designs as starting points, but the lack of isolating experiments limits how much can be taken away.

I would send the full manuscript to peer review because the clinical motivation is real and the framework is at least clearly described, even if the current evidence for the modules is thin. A referee could ask for the missing ablations and check whether the quantitative improvements survive proper testing.

Referee Report

2 major / 2 minor

Summary. The paper introduces AnF-DiffPET, a CT-conditioned diffusion model for low-dose PET denoising that integrates three modules—Anatomical-Frequency Guidance (AFG), Multi-Scale Cross-Transformer Reconstruction (MSCTR), and Frequency-Contrastive Hard Mining (FCHM)—to address insufficient anatomical guidance, unstable multi-scale propagation, and frequency-domain uptake recovery. It reports improved image fidelity, anatomical consistency, and quantitative metrics over CNN-, GAN-, transformer-, and diffusion-based baselines across four PET/CT datasets, with code and models to be released publicly.

Significance. If the reported gains prove robust and attributable to the proposed modules rather than base diffusion training, the work could meaningfully advance conditional diffusion approaches for medical image denoising by explicitly incorporating anatomy and frequency constraints, with downstream value for dose reduction in PET imaging. The commitment to public code release supports reproducibility and follow-on work.

major comments (2)

[Experiments] Experiments section: the central claim that AFG, MSCTR, and FCHM drive the observed gains in fidelity and consistency rests on comparisons to external baselines only; without ablation tables that isolate each module (e.g., full model vs. diffusion backbone alone, or successive addition of AFG/MSCTR/FCHM), attribution remains unverified and the improvements could arise from training protocol or dataset tuning instead.
[Experiments] Quantitative results (Tables 2–4): no error bars, standard deviations across runs, or statistical significance tests (e.g., paired t-tests or Wilcoxon) are reported for the metric improvements; this leaves open whether the gains over diffusion baselines survive proper testing and undermines the quantitative-fidelity claim.

minor comments (2)

[Abstract] Abstract and §1: the phrasing “frequency domain uptake recovery” is used without a brief definition or reference to the specific frequency bands or transforms employed; a short clarification would aid readers unfamiliar with frequency-guided diffusion.
The manuscript states that code will be released upon acceptance; confirming a specific repository link or DOI in the camera-ready version would strengthen the reproducibility claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and will revise the manuscript to strengthen the experimental validation.

read point-by-point responses

Referee: [Experiments] Experiments section: the central claim that AFG, MSCTR, and FCHM drive the observed gains in fidelity and consistency rests on comparisons to external baselines only; without ablation tables that isolate each module (e.g., full model vs. diffusion backbone alone, or successive addition of AFG/MSCTR/FCHM), attribution remains unverified and the improvements could arise from training protocol or dataset tuning instead.

Authors: We agree that internal ablation studies are required to attribute gains specifically to AFG, MSCTR, and FCHM rather than other factors. The original submission emphasized external baseline comparisons; we will add ablation tables in the revision showing the base diffusion backbone alone and the effects of successively incorporating each proposed module. revision: yes
Referee: [Experiments] Quantitative results (Tables 2–4): no error bars, standard deviations across runs, or statistical significance tests (e.g., paired t-tests or Wilcoxon) are reported for the metric improvements; this leaves open whether the gains over diffusion baselines survive proper testing and undermines the quantitative-fidelity claim.

Authors: We concur that variability measures and statistical tests are essential for robust quantitative claims. The revised manuscript will report standard deviations across multiple independent runs via error bars and include paired t-tests (or Wilcoxon signed-rank tests where appropriate) to assess statistical significance of improvements over diffusion baselines. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No derivation chain present; purely empirical DL method

full rationale

The paper describes a diffusion-based neural network architecture with three proposed modules (AFG, MSCTR, FCHM) and reports empirical gains on four datasets versus baselines. No mathematical derivation, first-principles result, or predictive equation is claimed or present. Performance numbers are obtained by training and evaluating on the datasets, which is standard empirical practice and does not constitute a derivation that reduces to its inputs by construction. Self-citations, if any, are not load-bearing for any claimed derivation.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only view yields no explicit free parameters, axioms, or invented entities; the framework implicitly assumes CT provides reliable anatomical ground truth and that frequency-domain contrastive mining improves uptake recovery without introducing new conserved quantities.

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Cite this review

Pith. "Pith review of AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising." pith.science (2026). https://pith.science/paper/4VBCBFSV

@misc{pith2026260700509,
  author       = {Pith},
  title        = {Pith review of: AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4VBCBFSV}},
  note         = {Machine review of arXiv:2607.00509}
}

read the original abstract

Positron emission tomography (PET) provides essential functional information for disease assessment, however reducing injected activity or acquisition time produces low-dose (LD) PET with stronger count dependent noise and less reliable uptake quantification. Diffusion models offer a promising solution for PET denoising by progressively recovering high-dose (HD) PET images from LD inputs. However, LD-to-HD PET denoising is still challenging due to insufficient anatomical guidance, unstable multi-scale feature propagation, and uncertain frequency domain uptake recovery. We propose AnF-DiffPET, an anatomy- and frequency-guided diffusion framework for computed tomography (CT) conditioned LD PET denoising. The framework integrates Anatomical-Frequency Guidance (AFG), Multi-Scale Cross-Transformer Reconstruction (MSCTR), and Frequency-Contrastive Hard Mining (FCHM) to enhance anatomy aware feature modulation and frequency domain consistency during denoising. Experimental results across four PET/CT datasets show that the proposed method improves image fidelity, anatomical consistency, and quantitative fidelity over representative CNN-based, GAN-based, transformer-based, and diffusion-based methods. The code and trained models will be publicly released upon acceptance.

Figures

Figures reproduced from arXiv: 2607.00509 by the authors.

**Figure 1.** Overall framework of AnF-DiffPET for CT conditioned LD PET denoising. (a) Training workflow with HD PET diffusion and LD PET/CT conditioned denoising. (b) Inference workflow using only LD PET and CT to generate the denoised HD PET image. (c) AFG module with cascaded FCA blocks for CT derived anatomical-frequency guidance. (d) FCA block for frequency domain query-key interaction and spatial domain value modulation. (… view at source ↗

**Figure 2.** MSCTR module for decoder-stage multi-scale feature reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** FCHM hard-negative sensitivity on HECKTOR. Three panels show [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Annotated heatmaps of performance gaps between AnF-Di [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Representative HECKTOR PET denoising case. Columns show the LD PET input, outputs from compared methods, AnF-Di [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Representative PET/CT denoising cases on NaF, PSMA, and RIDER. From top to bottom, the three case groups correspond to NaF, PSMA, and RIDER, respectively. Columns show the ground truth HD PET image, LD PET input, outputs from competing methods, and AnF-DiffPET. For eac…

**Figure 7.** Figure 7: Representative ablation case with PET visualization. Columns show [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: Grouped bar visualization for isolating MSCTR on HECKTOR. Di [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

[1]

G. K. von Schulthess, H. C. Steinert, T. F. Hany, Integrated pet/ct: current applications and future directions, Radiol- ogy 238 (2) (2006) 405–422

work page 2006
[2]

S. Basu, T. C. Kwee, S. Surti, E. A. Akin, D. Yoo, A. Alavi, Fundamentals of pet and pet/ct imaging, Annals of the New York Academy of Sciences 1228 (1) (2011) 1–18

work page 2011
[3]

T. M. Blodgett, C. C. Meltzer, D. W. Townsend, Pet/ct: form and function, Radiology 242 (2) (2007) 360–385

work page 2007
[4]

X. Tian, S. Chen, Y . Wang, D. Han, Y . Lin, J. Zhao, J.- C. Chen, Deep residual-in-residual model-based pet im- age super-resolution with motion blur, Electronics 13 (13) (2024) 2582

work page 2024
[5]

Tivnan, Q

M. Tivnan, Q. Li, Generative super-resolution pet imaging with fourier diffusion models, in: Medical Imaging 2025: Physics of Medical Imaging, V ol. 13405, SPIE, 2025, pp. 196–201

work page 2025
[6]

Z. Yang, Y . Zhou, H. Chen, H. Zhang, D. Zhao, B. Wei, Y . Xu, Unipet: A universal network for high-quality pet image denoising across varied dose reduction factors, Medical Image Analysis (2026) 104059

work page 2026
[7]

C. D. Pain, G. F. Egan, Z. Chen, Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement, European Journal of Nuclear Medicine and Molecular Imaging 49 (9) (2022) 3098–

work page 2022
[8]

doi:10.1007/s00259-022-05746-4

work page doi:10.1007/s00259-022-05746-4

Show all 26 references

[9]

A. M. Alessio, P. E. Kinahan, T. K. Lewellen, Model- ing and incorporation of system response functions in 3-d whole body pet, IEEE transactions on medical imaging 25 (7) (2006) 828–837

2006
[10]

Cloquet, F

C. Cloquet, F. Sureau, M. Defrise, G. Van Simaeys, N. Trotta, S. Goldman, Non-gaussian space-variant res- olution modelling for list-mode reconstruction, Physics in Medicine & Biology 55 (17) (2010) 5045–5066

2010
[11]

J. Kim, J. K. Lee, K. M. Lee, Accurate image super- resolution using very deep convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1646–1654

2016
[12]

B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee, Enhanced deep residual networks for single image super-resolution, in: Proceedings of the IEEE conference on computer vi- sion and pattern recognition workshops, 2017, pp. 136– 144

2017
[13]

X. Wang, K. Yu, S. Wu, J. Gu, Y . Liu, C. Dong, Y . Qiao, C. C. Loy, ESRGAN: Enhanced super-resolution gener- ative adversarial networks, in: Proceedings of the Eu- ropean Conference on Computer Vision (ECCV) Work- shops, 2018. 10

2018
[14]

X. Chen, Y . Qiu, W. Liang, G. Zhou, S. Xie, Spatial infor- mation regularized tensor decomposition framework for super-resolution reconstruction of medical mri and radio- graphs, IEEE Transactions on Computational Imaging 8 (2022) 865–878

2022
[15]

J. Cui, Y . Xie, N. Guo, Y . Feng, Q. Li, Pet image denoising using consistent denoising diffusion model (2024)

2024
[16]

Liang, J

J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timo- fte, SwinIR: Image restoration using swin transformer, in: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021, pp. 1833–1844

2021
[17]

J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, Advances in neural information processing sys- tems 33 (2020) 6840–6851

2020
[18]

S. Pan, T. Wang, R. L. Qiu, M. Axente, C.-W. Chang, J. Peng, A. B. Patel, J. Shelton, S. A. Patel, J. Roper, et al., 2d medical image synthesis using transformer-based de- noising diffusion probabilistic model, Physics in Medicine & Biology 68 (10) (2023) 105004

2023
[19]

G.-H. Liu, A. Vahdat, D.-A. Huang, E. A. Theodorou, W. Nie, A. Anandkumar, I2SB: Image-to-image schrödinger bridge, arXiv preprint arXiv:2302.05872 (2023)

2023
[20]

J. Liu, Q. Wang, H. Fan, Y . Wang, Y . Tang, L. Qu, Resid- ual denoising diffusion models, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 2773–2783

2024
[21]

Y . Li, Y . Wu, Y . Lai, M. Hu, X. Yang, Meddinov3: How to adapt vision foundation models for medical image seg- mentation?, arXiv preprint arXiv:2509.02379 (2025)

2025
[22]

Saeed, S

N. Saeed, S. Hassan, S. Hardan, A. Aly, D. Taratynova, et al., A multimodal and multi-centric head and neck cancer dataset for segmentation, diagnosis and out- come prediction, hECKTOR 2025 dataset paper (2025). arXiv:2509.00367, doi:10.48550/arXiv.2509.00367. URLhttps://arxiv...

2025 doi
[23]

Jeblick, B

K. Jeblick, B. Schachtner, A. Mittermeier, J. Dexl, P. Wesp, T. Küstner, S. Gatidis, et al., A whole-body psma-pet/ct dataset with manually annotated tumor lesions (2026). doi:10.7937/R7EP-3X37. URLhttps://www.cancerimagingarchive.net/ collection/PSMA-PET-CT-Lesions/

2026 doi
[24]

K. A. Kurdziel, A. B. Apolo, L. Lindenberg, E. Mena, Y . Y . McKinney, S. S. Adler, B. Turk- bey, et al., Data from naf prostate (2015). doi:10.7937/K9/TCIA.2015.ISOQTHKO. URLhttps://www.cancerimagingarchive.net/ collection/naf-prostate/

2015 doi
[25]

P. Muzi, M. Wanner, P. Kinahan, Data from rider lung pet-ct (2015). doi:10.7937/K9/TCIA.2015.OFIP7TVM. URLhttps://www.cancerimagingarchive.net/ collection/rider-lung-pet-ct/

2015 doi
[26]

Z. Han, Y . Wang, L. Zhou, P. Wang, B. Yan, J. Zhou, Y . Wang, D. Shen, Contrastive diffusion model with auxil- iary guidance for coarse-to-fine pet reconstruction (2023). arXiv:2308.10157. URLhttps://arxiv.org/abs/2308.10157 11

2023

Pith tools

Reviewed July 2, 2026 · model on record in the stance chip above.

[1] [1]

G. K. von Schulthess, H. C. Steinert, T. F. Hany, Integrated pet/ct: current applications and future directions, Radiol- ogy 238 (2) (2006) 405–422

work page 2006

[2] [2]

S. Basu, T. C. Kwee, S. Surti, E. A. Akin, D. Yoo, A. Alavi, Fundamentals of pet and pet/ct imaging, Annals of the New York Academy of Sciences 1228 (1) (2011) 1–18

work page 2011

[3] [3]

T. M. Blodgett, C. C. Meltzer, D. W. Townsend, Pet/ct: form and function, Radiology 242 (2) (2007) 360–385

work page 2007

[4] [4]

X. Tian, S. Chen, Y . Wang, D. Han, Y . Lin, J. Zhao, J.- C. Chen, Deep residual-in-residual model-based pet im- age super-resolution with motion blur, Electronics 13 (13) (2024) 2582

work page 2024

[5] [5]

Tivnan, Q

M. Tivnan, Q. Li, Generative super-resolution pet imaging with fourier diffusion models, in: Medical Imaging 2025: Physics of Medical Imaging, V ol. 13405, SPIE, 2025, pp. 196–201

work page 2025

[6] [6]

Z. Yang, Y . Zhou, H. Chen, H. Zhang, D. Zhao, B. Wei, Y . Xu, Unipet: A universal network for high-quality pet image denoising across varied dose reduction factors, Medical Image Analysis (2026) 104059

work page 2026

[7] [7]

C. D. Pain, G. F. Egan, Z. Chen, Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement, European Journal of Nuclear Medicine and Molecular Imaging 49 (9) (2022) 3098–

work page 2022

[8] [8]

doi:10.1007/s00259-022-05746-4

work page doi:10.1007/s00259-022-05746-4