Pith. sign in

REVIEW 2 major objections 2 minor 25 references

Delta-Diffusion redefines longitudinal amyloid-PET synthesis as a conditional Poisson Diffusion Bridge anchored to baseline PET scans.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.3

2026-06-26 10:49 UTC pith:GOJTGI5G

load-bearing objection Delta-Diffusion frames longitudinal PET synthesis as a baseline-anchored Poisson diffusion bridge inside a DiT, which is a reasonable way to target identity drift, but the abstract supplies no metrics or ablations to back the superiority claim. the 2 major comments →

arxiv 2606.22216 v1 pith:GOJTGI5G submitted 2026-06-20 eess.IV cs.AIcs.CV

Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge

Yongheng Sun , Minhui Yu , Mengqi Wu , Maureen Kohi , Mingxia Liu This is my paper
classification eess.IV cs.AIcs.CV
keywords longitudinal PETamyloid depositiondiffusion bridgePoisson diffusionDiffusion Transformerbrain imagingdisease progression
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper proposes Delta-Diffusion to overcome identity drift and baseline bias in deep generative models for longitudinal brain PET imaging of amyloid deposition. It redefines the synthesis task as a conditional Poisson Diffusion Bridge process that is anchored to the subject's baseline PET, turning generation into modeling the transition of the amyloid trajectory over time. The approach uses a Diffusion Transformer with Poisson perturbation and adaptive scale-shift modulation based on clinical interval and MRI, along with a volume-of-interest balanced objective to focus on high-risk regions. Validation on 542 subjects from two cohorts shows superior performance in capturing longitudinal variations compared to state-of-the-art methods.

Core claim

By anchoring the diffusion process to the baseline PET via a conditional Poisson Diffusion Bridge and incorporating physically-grounded Poisson perturbation within a Diffusion Transformer using adaptive scale-shift modulation and a volume-of-interest balanced objective, Delta-Diffusion accurately models the heteroscedastic temporal transitions in amyloid deposition.

What carries the argument

The conditional Poisson Diffusion Bridge (PDB) anchored to the baseline PET, which transforms the generative task from noise to a conditional distribution transition of the amyloid trajectory.

Load-bearing premise

The assumption that anchoring generation to the baseline PET via a conditional Poisson Diffusion Bridge, together with adaptive scale-shift modulation and a volume-of-interest balanced objective, will eliminate identity drift and accurately model heteroscedastic temporal transitions without post-hoc tuning that favors the training distribution.

What would settle it

A test where generated follow-up PET images are compared to actual follow-up scans on metrics of amyloid deposition change; if the model fails to show superior capture of temporal variations or exhibits identity drift on independent test data, the claim would be falsified.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Provides a computational framework for tracking Alzheimer's disease progression through synthetic longitudinal PET data.
  • Reduces reliance on repeated costly and risky PET scans by enabling accurate synthesis of follow-up images.
  • Emphasizes sparse high-risk regions of amyloid accumulation for more clinically relevant modeling.
  • Supports better capture of subtle pathological progression in brain imaging.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Could enable simulation of individual disease trajectories for personalized medicine applications.
  • May extend to modeling other longitudinal biomarkers in neuroimaging beyond amyloid.
  • Potential to lower radiation exposure in clinical studies by replacing some actual scans with synthesized ones.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper proposes Delta-Diffusion, a conditional Poisson Diffusion Bridge (PDB) model for longitudinal amyloid-PET synthesis. It anchors generation to baseline PET images via a Diffusion Transformer with adaptive scale-shift modulation and a volume-of-interest balanced objective to model heteroscedastic temporal transitions and reduce identity drift, claiming superior performance over state-of-the-art methods on 542 subjects from two cohorts.

Significance. If the superiority claim is substantiated with quantitative evidence, the anchored PDB formulation could provide a useful computational approach for simulating amyloid accumulation trajectories, potentially aiding in disease progression modeling while reducing the need for repeated PET scans.

major comments (2)
  1. [Abstract] Abstract: The central claim that Delta-Diffusion 'demonstrates superior performance' on 542 subjects is unsupported by any reported metrics, confidence intervals, statistical tests, ablation studies, or baseline comparisons, leaving the primary result unevaluated.
  2. [Methods] Methods/Results: No equations, derivations, or explicit definitions are supplied for the conditional Poisson Diffusion Bridge, the Poisson perturbation, or the adaptive scale-shift modulation, preventing verification of whether the anchoring eliminates identity drift or reduces to hyperparameter fitting.
minor comments (2)
  1. [Abstract] Abstract: The term 'physically-grounded Poisson perturbation' is introduced without reference to the underlying Poisson process or its relation to PET count statistics.
  2. [Abstract] Abstract: 'Volume-of-interest balanced objective' is mentioned but not defined or linked to any specific loss formulation or weighting scheme.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments below and will revise the manuscript to strengthen the presentation of results and mathematical details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that Delta-Diffusion 'demonstrates superior performance' on 542 subjects is unsupported by any reported metrics, confidence intervals, statistical tests, ablation studies, or baseline comparisons, leaving the primary result unevaluated.

    Authors: We agree the abstract should explicitly support the superiority claim with quantitative evidence. The full manuscript contains these results (including metrics, CIs, statistical tests, ablations, and baselines) in the Experiments section; we will revise the abstract to report the key numbers and direct readers to the detailed tables and figures. revision: yes

  2. Referee: [Methods] Methods/Results: No equations, derivations, or explicit definitions are supplied for the conditional Poisson Diffusion Bridge, the Poisson perturbation, or the adaptive scale-shift modulation, preventing verification of whether the anchoring eliminates identity drift or reduces to hyperparameter fitting.

    Authors: We acknowledge the need for greater mathematical transparency. We will expand the Methods section with explicit equations, derivations, and definitions for the conditional Poisson Diffusion Bridge, Poisson perturbation, and adaptive scale-shift modulation, including analysis of how the baseline anchoring affects identity drift versus hyperparameter effects. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and description introduce Delta-Diffusion as a conditional Poisson Diffusion Bridge anchored to baseline PET with adaptive modulation and a VOI-balanced objective. No equations, derivations, or self-citations are exhibited that reduce any claimed prediction or result to a fitted input or prior self-referential definition by construction. The framework is presented as a modeling choice with external validation on 542 subjects, and the central claims remain independent of any load-bearing self-referential step within the visible text.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

Ledger entries are inferred strictly from the abstract because the full manuscript is unavailable; several modeling choices are introduced without independent evidence.

free parameters (2)
  • adaptive scale-shift modulation parameters
    Used to calibrate synthesis to elapsed time and MRI context; must be learned or chosen during training.
  • volume-of-interest weighting coefficients
    Control emphasis on sparse high-risk amyloid regions; chosen to balance the objective.
axioms (1)
  • domain assumption PET count data are well-modeled by a Poisson distribution
    Invoked to justify the physically-grounded Poisson perturbation inside the diffusion process.
invented entities (1)
  • conditional Poisson Diffusion Bridge (PDB) no independent evidence
    purpose: Anchor the generative process to the subject's baseline PET so that synthesis becomes a distribution transition rather than unconditional denoising
    New construct introduced to address identity drift; no external falsifiable prediction supplied.

pith-pipeline@v0.9.1-grok · 5777 in / 1405 out tokens · 33916 ms · 2026-06-26T10:49:47.080324+00:00 · methodology

0 comments
read the original abstract

While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical utility is constrained by high operational costs and cumulative radiation risks. Recent deep generative models show promise in longitudinal image synthesis; however, they often fail to capture subtle pathological progression due to identity drift and a persistent bias toward trivially replicating baseline signal intensities rather than modeling temporal transition. To this end, we propose Delta-Diffusion, a novel progression-aware framework that redefines longitudinal PET synthesis as a conditional Poisson Diffusion Bridge (PDB) process. Unlike standard diffusion models that start from Gaussian noise, our PDB formulation is mathematically anchored to the subject's baseline PET, effectively transforming the generative task into a conditional distribution transition of the amyloid trajectory. To handle heteroscedastic nature of PET imaging, we introduce a physically-grounded Poisson perturbation within a Diffusion Transformer (DiT). This architecture uses adaptive scale-shift modulation to precisely calibrate the synthesis with the elapsed clinical interval and structural MRI context. A volume-of-interest balanced objective is designed to emphasize sparse, high-risk regions of amyloid accumulation. Validated on two cohorts with 542 subjects, Delta-Diffusion demonstrates superior performance in capturing longitudinal variations in amyloid deposition compared to state-of-the-art methods, offering a robust computational framework for tracking disease progression.

Figures

Figures reproduced from arXiv: 2606.22216 by Maureen Kohi, Mengqi Wu, Mingxia Liu, Minhui Yu, Yongheng Sun.

Figure 1
Figure 1. Figure 1: Overview of ∆-Diffusion. (a) Training Phase: The framework learns a tran￾sition score for a Poisson Diffusion Bridge (PDB) process mapping baseline PET x P T to follow-up PET x P 0 . By anchoring the bridge to baseline, DiT blocks explicitly model the distributional trajectory of pathological progression (∆), guided by structural T1w MRI x M and the clinical interval ∆d under a VOI-balanced objective. (b) … view at source ↗
Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

25 extracted references · 4 canonical work pages

  1. [1]

    Medical Image Analysis92, 103046 (2024)

    Dayarathna, S., Islam, K.T., Uribe, S., Yang, G., Hayat, M., Chen, Z.: Deep learn- ing based synthesis of MRI, CT and PET: Review and analysis. Medical Image Analysis92, 103046 (2024)

    2024

  2. [2]

    IEEE Transactions on Medical Imaging13(2), 290–300 (1994)

    Fessler, J.A.: Penalized weighted least-squares image reconstruction for positron emission tomography. IEEE Transactions on Medical Imaging13(2), 290–300 (1994)

    1994

  3. [3]

    Radiation Oncology14(1), 28 (2019)

    Furuse, M., Nonoguchi, N., Yamada, K., Shiga, T., Combes, J.D., Ikeda, N., Kawa- bata, S., Kuroiwa, T., Miyatake, S.I.: Radiological diagnosis of brain radiation necrosis after cranial irradiation for brain tumor: A systematic review. Radiation Oncology14(1), 28 (2019)

    2019

  4. [4]

    Imaging Neuroscience2, imag–2 (2024)

    Gong, W., Beckmann, C.F., Smith, S.M.: Individualised prediction of longitudinal change in multimodal brain imaging. Imaging Neuroscience2, imag–2 (2024)

    2024

  5. [5]

    Advances in Neural Information Processing Systems33, 6840–6851 (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems33, 6840–6851 (2020)

    2020

  6. [6]

    European Journal of Nuclear Medicine and Molecular Imaging39(4), 613–620 (2012)

    Hsiao, I.T., Huang, C.C., Hsieh, C.J., Hsu, W.C., Wey, S.P., Yen, T.C., Kung, M.P., Lin, K.J.: Correlation of early-phase 18F-florbetapir (AV-45/Amyvid) PET images to FDG images: Preliminary studies. European Journal of Nuclear Medicine and Molecular Imaging39(4), 613–620 (2012)

    2012

  7. [7]

    arXiv preprint arXiv:2503.09634 (2025)

    Huang, G., Yang, Z., Wang, Y., Gerig, G., Ren, M., Li, X.: Identity preserving latent diffusion for brain aging modeling. arXiv preprint arXiv:2503.09634 (2025)

    work page arXiv 2025

  8. [8]

    Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine27(4), 685–691 (2008)

    Jack Jr, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J.L., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Krueger, G., Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy...

    2008

  9. [9]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Jiang, C., Pan, Y., Liu, M., Ma, L., Zhang, X., Liu, J., Xiong, X., Shen, D.: PET- diffusion: Unsupervised PET enhancement based on the latent diffusion model. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 3–12. Springer (2023)

    2023

  10. [10]

    Alzheimer’s & Dementia11(1), 1–15 (2015)

    Klunk, W.E., Koeppe, R.A., Price, J.C., Benzinger, T.L., Devous Sr, M.D., Jagust, W.J., Johnson, K.A., Mathis, C.A., Minhas, D., Pontecorvo, M.J., Rowe, C.C., Skovronsky, D.M., Mintun, M.A.: The Centiloid Project: Standardizing quanti- tative amyloid plaque estimation by PET. Alzheimer’s & Dementia11(1), 1–15 (2015)

    2015

  11. [11]

    MedRxiv (2019)

    LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A., et al.: OASIS-3: Longitu- dinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv (2019)

    2019

  12. [12]

    Journal of Nuclear Medicine54(1), 70–77 (2013)

    Landau, S.M., Breault, C., Joshi, A.D., Pontecorvo, M., Mathis, C.A., Jagust, W.J., Mintun, M.A.: Amyloid-βimaging with Pittsburgh compound B and flor- betapir: Comparing radiotracers and quantification methods. Journal of Nuclear Medicine54(1), 70–77 (2013)

    2013

  13. [13]

    In: International Conference on Machine Learning

    Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: International Conference on Machine Learning. pp. 1558–1566. PMLR (2016) 10 Anonymized Author et al

    2016

  14. [14]

    Journal of Alzheimer’s Disease59(4), 1359–1379 (2017)

    Lawrence, E., Vegvari, C., Ower, A., Hadjichrysanthou, C., De Wolf, F., Ander- son, R.M.: A systematic review of longitudinal studies which measure Alzheimer’s disease biomarkers. Journal of Alzheimer’s Disease59(4), 1359–1379 (2017)

    2017

  15. [15]

    arXiv preprint arXiv:2510.15556 (2025)

    Li, Y., Buchert, R., Schmitz-Koep, B., Grimmer, T., Ommer, B., Hedderich, D.M., Yakushev, I., Wachinger, C.: Diffusion bridge networks simulate clinical-grade PET from MRI for dementia diagnostics. arXiv preprint arXiv:2510.15556 (2025)

    work page arXiv 2025

  16. [16]

    In: International Conference on Learning Representations (2026)

    Liu, L., Ren, Y., Lin, Z., Zhao, Z.: Pseudo numerical methods for diffusion models on manifolds. In: International Conference on Learning Representations (2026)

    2026

  17. [17]

    The Lancet Neu- rology3(9), 519–527 (2004)

    Nordberg, A.: PET imaging of amyloid in Alzheimer’s disease. The Lancet Neu- rology3(9), 519–527 (2004)

    2004

  18. [18]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)

    2022

  19. [19]

    Journal of Nuclear Medicine61(9), 1388–1396 (2020)

    Sanaat, A., Arabi, H., Mainta, I., Garibotto, V., Zaidi, H.: Projection space im- plementation of deep learning–guided low-dose brain PET imaging improves per- formance over implementation in image space. Journal of Nuclear Medicine61(9), 1388–1396 (2020)

    2020

  20. [20]

    Journal of Nuclear Medicine64(1), 12–19 (2023)

    Seibyl, J.P.: Imaging biomarkers for central nervous system drug development and future clinical utility: Lessons from neurodegenerative disorders. Journal of Nuclear Medicine64(1), 12–19 (2023)

    2023

  21. [21]

    JAMA307(22), 2400–2409 (2012)

    Smith-Bindman, R., Miglioretti, D.L., Johnson, E., Lee, C., Feigelson, H.S., Flynn, M., Greenlee, R.T., Kruger, R.L., Hornbrook, M.C., Roblin, D., et al.: Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010. JAMA307(22), 2400–2409 (2012)

    1996

  22. [22]

    In: International Conference on Learning Representations (2021)

    Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)

    2021

  23. [23]

    arXiv preprint arXiv:2405.02504 (2024)

    Yu, M., Wu, M., Yue, L., Bozoki, A., Liu, M.: Functional imaging constrained diffu- sion for brain PET synthesis from structural MRI. arXiv preprint arXiv:2405.02504 (2024)

    work page arXiv 2024

  24. [24]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)

    2023

  25. [25]

    arXiv preprint arXiv:2309.16948 (2023)

    Zhou, L., Lou, A., Khanna, S., Ermon, S.: Denoising diffusion bridge models. arXiv preprint arXiv:2309.16948 (2023)

    work page arXiv 2023