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REVIEW 3 major objections 6 minor 39 references

A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A two-stage diffusion model converts 18F-FDG PET sinograms into 18F-DOPA PET sinograms, beating four existing translation baselines on all reported metrics.

desk verdict Real sinogram-domain tracer conversion with an unvalidated refinement-condition distribution; worth refereeing but needs an extra experiment. read the letter →

arxiv 2506.16733 v2 pith:JLH7NKFM submitted 2025-06-20 eess.IV cs.CV

Fang Chen , Weifeng Zhang , Xingyu Ai , BingXuan Li , An Li , Qiegen Liu This is my paper · ORCID
classification eess.IVcs.CV
keywords PETtracerconversionsinogramsynthesisdiffusionmodelprior-guidedrefinementcoarse-to-finelearning18F-DOPA18F-FDGprojectiondomain
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

This paper tries to establish that a two-stage diffusion model called PJDM can convert 18F-FDG PET sinograms into 18F-DOPA PET sinograms more accurately than existing image-translation methods. Working directly on the raw projection data, before image reconstruction, is meant to preserve the scanner's original information and reduce errors that accumulate during reconstruction. The authors propose a coarse-to-fine pipeline: a supervised first stage produces an initial DOPA sinogram, and an unsupervised second stage refines it using a degraded version of that initial sinogram as guidance. If correct, the method would make DOPA-like PET information available without the costly synthesis and short transport window of the DOPA tracer, and it would demonstrate a practical way to combine a small paired dataset with a large unpaired target-domain dataset.

What carries the argument

The load-bearing mechanism is a cascaded generative model with two stages. Stage I is a supervised diffusion-bridge network that treats the input 18F-FDG sinogram as the endpoint of a diffusion process and the target 18F-DOPA sinogram as the start, using Doob's h-transform, a conditioning transform that forces the terminal state toward the given sinogram, to guide the bridge; sampling uses a higher-order hybrid sampler combining Euler-Maruyama and Heun steps. Stage II is a conditional denoising diffusion model whose input at each reverse step is the noisy DOPA sinogram concatenated with a degraded version of the Stage-I output, obtained by a degradation function R that applies Gaussian blurring and random contrast and brightness changes. The reverse process starts at an intermediate timestep t_prior = 185 so that only part of the noise is removed, letting the degraded prior hold structural information while the model restores detail. The central conditioning identity is concat(x_t, R(hat{x}_0)), which lets a model trained on real DOPA sinograms pull the coarse synthetic prediction back toward the DOPA manifold.

What would settle it

Compare PSNR and SSIM before and after Stage II on (a) degraded real DOPA sinograms and (b) degraded Stage-I synthetic outputs; if refinement only improves the real inputs or worsens the synthetic ones, the match between training and inference conditioning is the breaking point.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that prior-guided refinement in the projection domain improves PET tracer conversion. The proposed PJDM reports PSNR of 24.98 dB, SSIM of 0.812, and NRMSE of 0.064 on a test set of 141 paired scans, outperforming U-Net, CycleGAN, Pix2Pix, and cold diffusion on all three metrics; against the second-best method, cold diffusion, the gain is 1.12 dB in PSNR and 0.059 in SSIM. The ablation supports the two-stage design: coarse estimation alone reaches 24.25 dB PSNR, prior refinement alone 22.87 dB, and the combination 24.98 dB, with SSIM rising from 0.751 or 0.739 to 0.812. The first stage maps an 18F-FDG sinogram to an initial 18F-DOPA sinogram using a supervised diffusion bridge, and the second stage runs a conditional denoising process whose condition is the initial synthetic DOPA sinogram after Gaussian blurring and random contrast and brightness perturbations, with the reverse process started at an intermediate timestep. The paper claims this preserves overall structure while restoring fine detail, and that both the synthesized sinograms and their reconstructed images are the closest to ground truth among the compared methods.

Load-bearing premise

If the synthetic DOPA sinogram from Stage I is far from the real DOPA sinogram manifold, then the Stage II refiner, which was trained on degraded real DOPA sinograms, may fail to correct it and could even distort the result.

Editorial extensions

If this is right

  • If the result generalizes, clinics could generate DOPA-like PET sinograms from routine FDG scans, reducing the need for the harder-to-produce 18F-DOPA tracer.
  • The coarse-to-fine recipe provides a way to exploit a small paired dataset plus a large unpaired target-domain dataset, combining supervised structural mapping with unsupervised detail refinement.
  • Because conversion happens before reconstruction, the method could be integrated into a PET scanner's raw-data pipeline, potentially avoiding errors introduced when translating reconstructed images.
  • The reported gains over cold diffusion suggest that diffusion-based translation can be improved by adding a learned degradation prior rather than relying on arbitrary forward transforms alone.

Reading between the lines

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

  • A natural stress test the authors did not run: feed Stage II a degraded real DOPA sinogram versus a degraded synthetic one; if the gain over Stage I vanishes or reverses on synthetic inputs, the conditioning distribution is the fragile link.
  • The same two-stage pattern may transfer to other tracer pairs or projection-domain translation tasks, but the transfer likely depends on how close the coarse stage can get to the target manifold.
  • If the condition-distribution mismatch is real, a testable extension would be to train Stage II on degraded synthetic outputs from Stage I in addition to degraded real DOPA sinograms.
  • Reporting sinogram-domain and reconstructed-image metrics separately could reveal whether Stage II's gains are genuine projection-domain improvements or effects that only survive reconstruction.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes a prior-guided joint diffusion model (PJDM) for converting 18F-FDG PET sinograms into 18F-DOPA PET sinograms. The method has two stages: a supervised diffusion bridge model (built on DDBM) performs a coarse FDG-to-DOPA conversion using a higher-order hybrid sampler; a second, "prior refinement" stage uses a DDPM conditioned on a degraded (blurred and contrast/brightness-perturbed) version of the Stage-I output to refine the sinogram. Experiments on a dataset of 196 PD patients, with 2,643 paired FDG/DOPA scans and 3,877 unpaired DOPA scans for training and 141 paired test scans from an all-digital PET system, report that PJDM achieves PSNR 24.98 dB, SSIM 0.812, and NRMSE 0.064, outperforming U-Net, CycleGAN, Pix2Pix, and cold diffusion. An ablation study (Table II) shows that combining coarse estimation (CE) and prior refinement (PR) improves PSNR from 24.25 to 24.98 and SSIM from 0.751 to 0.812 compared to CE alone.

Significance. If the reported results are robust, the paper addresses a clinically meaningful problem: generating DOPA-equivalent PET data from widely available FDG scans, motivated by the complex synthesis and short half-life of 18F-DOPA. The projection-domain modeling choice is sensible for PET data, and the two-stage supervised-plus-unsupervised design is a plausible way to reduce the need for large paired datasets. The authors provide a public code repository, which supports reproducibility. However, the evidence for the central two-stage improvement claim rests on an unvalidated distributional assumption in the refinement stage and on metric averages without error bars or significance tests. The contribution is incremental and potentially useful to the PET imaging community, but the findings are not yet convincingly established.

major comments (3)
  1. [Section III-C, Eqs. (9)-(13) and Algorithm 1] The prior refinement model is trained with the condition d = R(x_0) where x_0 is drawn from the real DOPA sinogram distribution, but at inference the condition is d = R(CE(FDG)), where CE(FDG) is a synthetic DOPA sinogram produced by Stage I. The paper provides no evidence that R(CE(FDG)) lies in the training condition distribution, and no ablation that trains or evaluates Stage II on synthetic conditions. Since Table II attributes a PSNR gain of 0.73 dB and an SSIM gain of 0.061 specifically to the CE+PR combination, this train/inference mismatch is load-bearing for the main claim. Please add a quantitative comparison of the two condition distributions (e.g., distribution distances or per-pixel statistics) and an ablation in which Stage II is exposed to synthetic conditions during training, or otherwise justify that the mismatch is negligible.
  2. [Section IV-D, Tables I and II] All reported metrics are single averages without error bars, standard deviations, or statistical significance tests. The key improvements—PJDM over CD (PSNR +1.12 dB, SSIM +0.059) and CE+PR over CE alone (PSNR +0.73 dB, SSIM +0.061)—may be within intersubject variability. Please report per-patient or per-scan results with variance, run paired significance tests (e.g., Wilcoxon signed-rank) between methods, and provide confidence intervals for the mean metrics in Tables I and II.
  3. [Section IV-A] The data description does not state whether the 141 paired test scans are from patients disjoint from the 196 patients used to construct the training/refinement sets. If the test set is not patient-disjoint, the results are optimistically biased. The test set is also identified as coming from an all-digital PET system (Brain PET B320), but the scanner provenance of the training scans is not reported. Please clarify the patient-level and scanner-level split, and assess whether a domain shift between training and test scanners affects the comparison.
minor comments (6)
  1. [Section III-B] After Eq. (4), the text says the supervised training performs "the initial modality conversion from 18F-DOPA sinograms to 18F-FDG sinograms," which is the reverse of the stated task (FDG-to-DOPA); please correct the direction throughout.
  2. [Equations generally] In the submitted version, several equations (e.g., Eqs. (1)-(7) and (14)-(16)) appear garbled or mis-rendered, making it impossible to verify the exact definitions and indices. Please ensure the final camera-ready version typesets all equations cleanly.
  3. [Section III-C, Eq. (13)] The paper refers to the neural network output as noise prediction (epsilon) in Eq. (10), but Eq. (13) uses this output in a manner that mixes epsilon- and x_0-prediction notation. Please state explicitly which parameterization is used for the prior refinement model.
  4. [Algorithm 1] The discretization of the coarse estimation sampler, including the role of the step-ratio parameter m and the Heun step, is not defined unambiguously in the pseudocode; a table of symbols or a more explicit time-stepping description would improve reproducibility.
  5. [Fig. 8 and Fig. 11] The claims that PJDM's profile lines are "closest to the ground truth" are made qualitatively; please report a quantitative discrepancy metric (e.g., mean absolute error along the profile) to support these statements.
  6. [Table II] The ablation shows that PR alone achieves PSNR 22.87, notably worse than CE alone (24.25), yet the narrative only emphasizes that CE+PR is best; a brief discussion of why PR alone underperforms would make the two-stage mechanism more interpretable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the two-stage synthesis is evaluated against external DOPA ground truth, and no prediction reduces to a fitted input.

full rationale

PJDM's core conversion is trained as a supervised diffusion bridge model (Eq. 4) on paired FDG/DOPA sinograms, and the refinement module is trained on real DOPA sinograms with a self-degradation condition (Eqs. 9-10). At inference, the refinement condition is d = R(CE(FDG)), which is not identical to the training condition d = R(x0) for real x0; this is a potential train/test distribution mismatch and a generalization risk, not a definitional circularity. The claimed gains are measured on an independent test set against true DOPA sinograms (Tables I and II), so the central result is not forced by the model's own outputs or by a fitted parameter renamed as a prediction. The self-citations [27] and [28] are background on residual diffusion and Brownian-bridge models and are not load-bearing for the paper's novelty claim. Therefore no circular step is identifiable from the paper's equations or citations.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The pipeline uses standard diffusion machinery and no new physical entities. The main load-bearing choices are the sampling hyperparameters and the untested transfer of the refinement condition from real to synthetic DOPA sinograms.

free parameters (4)
  • guidance strength w = not reported numerically
    Controls drift strength in the ODE sampler (Eq. (3)); affects the diversity and quality of the coarse-stage output.
  • step ratio m = not reported numerically
    Defines step-size ratio between sampling time steps in the Karras-type higher-order sampler.
  • prior start timestep t_prior = 185
    Start of the refinement reverse process; trades off detail retention against noise removal; set once for all experiments.
  • degradation function R = Gaussian blur plus random contrast/brightness, ranges not specified
    Used to produce the conditional prior at inference; parameters are chosen by hand and not reported.
assumptions (4)
  • standard math Doob's h-transform construction of the diffusion bridge is valid for the chosen SDE
    Basis for the coarse estimation model in Eqs. (1)-(3).
  • standard math Score function can be approximated by denoising score matching with a U-Net
    Required for reverse sampling in Stage I.
  • domain assumption A learnable deterministic mapping exists from FDG to DOPA sinograms
    The paired supervised stage assumes FDG sinogram information is sufficient to predict DOPA distribution; introduced in Section III.B.
  • domain assumption Refinement model trained on degraded real DOPA sinograms transfers to degraded synthetic DOPA sinograms
    Stage II is trained on R(x0) for real x0, but at inference uses R(coarse output); no distribution-shift validation is provided; Section III.C and IV.B.

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Cite this review

Pith. "Pith review of A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion." pith.science (2026). https://pith.science/paper/JLH7NKFM

@misc{pith2026250616733,
  author       = {Pith},
  title        = {Pith review of: A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JLH7NKFM}},
  note         = {Machine review of arXiv:2506.16733}
}
read the original abstract

Positron emission tomography (PET) is widely used to assess metabolic activity, but its application is limited by the availability of radiotracers. 18F-labeled fluorodeoxyglucose (18F-FDG) is the most commonly used tracer but shows limited effectiveness for certain tumors. In contrast, 6-18F-fluoro-3,4-dihydroxy-L-phenylalanine (18F-DOPA) offers higher specificity for neuroendocrine tumors and neurological disorders. However, the complexity of its synthesis process and constraints on transportation time have limited its clinical application. Among different forms of raw data acquired by the scanner, sinogram is a commonly used representation in PET imaging. Therefore, modeling in projection domain enables more direct utilization of the original information, potentially reducing the accumulation errors during the image reconstruction process. Inspired by these factors, this study proposes a prior-guided joint diffusion model (PJDM) for transforming 18F-FDG PET sinograms into 18F-DOPA PET sinograms. During inference, an initial synthetic 18F-DOPA PET sinogram is first generated using a higher-order hybrid sampler. This sinogram is then degraded and serves as an additional condition to guide the iterative refinement process. Experimental results demonstrated that PJDM effectively improved both sinogram quality and the final synthetic outcomes. The code is available at: https://github.com/yqx7150/PJDM.

Figures

Figures reproduced from arXiv: 2506.16733 by the authors.

Figure 1
Figure 1. PET tracer conversion between ¹⁸F-FDG and ¹⁸F-DOPA PET in image domain (a) and projection domain (b), respectively. The proposed model employs a coarse-to-fine learning strat￾egy, consisting of two stages. At the first stage, the model uti￾lizes supervised learning with a limited number of paired sam￾ples to perform a coarse modality conversion, mapping ¹⁸F￾FDG sinograms to ¹⁸F-DOPA sinograms. At the second stage, a… view at source ↗
Figure 2
Figure 2. The fundamental training paradigms of supervised and unsupervised learning. Unsupervised diffusion models, such as denoising diffusion probabilistic models (DDPMs) [25] and score-based generative models [26], mainly focus on learning data distributions to gen￾erate high-quality samples. These models do not rely on labels but learn by leveraging the inherent characteristics of the data. However, in order to make the … view at source ↗
Figure 3
Figure 3. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Main process of PJDM. (a) Simple training process of the coarse estimation model, (b) Simple training process of the prior refinement model, (c) Simple testing process of PJDM. Accordingly, this study proposes a cascaded mechanism that integrates supervised and unsuper…
Figure 5
Figure 5. Figure 5: The overall framework of PJDM. First, the coarse estimation model D synthesizes an initial ¹⁸F-DOPA sinogram from the ¹⁸F-FDG sinogram while preserving the spatial structure. Then, the ¹⁸F-DOPA sinogram, after undergoing Gaussian blurring along with random contrast an…
Figure 6
Figure 6. Figure 6: The synthesized results of PJDM. The first column on the left shows the input image (¹⁸F-FDG), the second column presents the target image (¹⁸F-DOPA), and the third column displays the synthesized ¹⁸F-DOPA image produced by the model [PITH_FULL_IMAGE:figures/full_fig_…
Figure 7
Figure 7. Figure 7: Comparison of synthesized results using different methods. The first row shows the sinograms, the second row presents the synthesized images, the third row displays the residuals, and the last row presents the zoomed-in images of key regions. The profile lines of stria…
Figure 8
Figure 8. Figure 8: The profile lines of the striatum region. The results indicate that the recovery values obtained by PJDM are closest to the ground truth. In addition, a dedicated analysis is conducted on the synthe￾sized sinogram results. In [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Comparison of sinogram synthesis results using different methods. The first row shows the synthesized sinograms, while the second row presents the corresponding zoomed-in regions. Ablation Studies: To evaluate the effectiveness of each com￾ponent, ablation studies are …
Figure 10
Figure 10. Figure 10: The comparison results of ablation studies. The details of the image enhanced after applying the PR method. TABLE II QUANTITATIVE COMPARISON OF TRACER CONVERSION METRICS AMONG DIFFERENT MODULES Metric (w/o) CE (w/o) PR PJDM PSNR 24.25 22.87 24.98 SSIM 0.751 0.739 0.…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.