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arxiv: 2602.12755 · v3 · pith:5HTYBDGXnew · submitted 2026-02-13 · 💻 cs.CV

Towards reconstructing experimental sparse-view X-ray CT data with diffusion models

Nelas J. Thomsen , Xinyuan Wang , Felix Lucka , Ezgi Demircan-Tureyen This is my paper

Pith reviewed 2026-05-21 12:44 UTC · model grok-4.3

classification 💻 cs.CV
keywords diffusion modelssparse-view CTdomain shiftX-ray computed tomographyexperimental datainverse problemsCT reconstruction
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The pith

Diffusion priors trained on diverse synthetic data reconstruct experimental sparse-view CT scans effectively.

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

The paper tests whether diffusion models trained on synthetic data can serve as effective priors for reconstructing experimental sparse-view X-ray CT scans. It examines the effects of domain shift between synthetic training data and a physical phantom, as well as forward model mismatch in the reconstruction process. Results indicate that severe domain mismatch leads to model collapse, but diverse priors perform as well as or better than narrowly matched ones. Annealed likelihood weight schedules help mitigate artifacts from forward model mismatch while improving efficiency. This work highlights that benefits seen in synthetic settings do not directly carry over to real experimental data.

Core claim

Diffusion-based priors trained on synthetic image data sets with different degrees of domain shift can be used in a Decomposed Diffusion Sampling scheme to reconstruct sparse-view CT data from a physical phantom. Diverse priors match or exceed well-matched narrow priors, and annealed schedules mitigate forward model mismatch artifacts.

What carries the argument

Decomposed Diffusion Sampling scheme using diffusion priors on sparse-view CT data with annealed likelihood weight schedules to address domain and forward model mismatch.

Load-bearing premise

The physical phantom and the chosen synthetic training sets with varying degrees of domain shift are sufficient to represent the mismatch that would occur with real clinical or industrial CT data.

What would settle it

Applying the method to clinical patient CT data and observing persistent hallucinations or reconstruction failure despite diverse priors would show the assumption does not hold for practical cases.

Figures

Figures reproduced from arXiv: 2602.12755 by Ezgi Demircan-Tureyen, Felix Lucka, Nelas J. Thomsen, Xinyuan Wang.

Figure 1
Figure 1. Figure 1: PSNR (dB) as a function of the number of projections across four different test domains for the reconstructions obtained [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Line profiles from reconstructions shown in Fig. 2 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: PSNR (dB) vs number of projections for three reso [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original abstract

Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studies consider synthetic data, it is not clear whether training data mismatch (``domain shift'') or forward model mismatch complicate their successful application to experimental data. We measured CT data from a physical phantom resembling the synthetic Shepp-Logan phantom and trained diffusion priors on synthetic image data sets with different degrees of domain shift towards it. Then, we employed the priors in a Decomposed Diffusion Sampling scheme on sparse-view CT data sets with increasing difficulty leading to the experimental data. Our results reveal that domain shift plays a nuanced role: while severe mismatch causes model collapse and hallucinations, diverse priors match or exceed well-matched but narrow priors. Forward model mismatch pulls the image samples away from the prior manifold, which causes artifacts but can be mitigated with annealed likelihood weight schedules that also increase computational efficiency. Overall, we demonstrate that performance gains do not immediately translate from synthetic to experimental data, and future development must validate against real-world benchmarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript investigates the application of diffusion model priors to experimental sparse-view X-ray CT reconstruction. The authors acquire data from a physical phantom resembling the Shepp-Logan phantom, train diffusion priors on synthetic image datasets with controlled degrees of domain shift, and apply them via Decomposed Diffusion Sampling on sparse-view measurements of increasing difficulty up to the experimental case. They report that domain shift plays a nuanced role—severe mismatch leads to collapse and hallucinations while diverse priors match or exceed narrow but well-matched ones—and that annealed likelihood weight schedules mitigate artifacts from forward-model mismatch, though synthetic performance gains do not translate directly to experimental data.

Significance. If the empirical observations hold under broader validation, the work is significant for highlighting practical challenges in transferring generative priors from synthetic to real CT data. It supplies concrete evidence on the effects of domain shift and a mitigation strategy (annealed schedules) that also improves efficiency, offering guidance for future diffusion-based inverse-problem solvers in medical and industrial imaging.

major comments (2)
  1. [Experimental Setup and Results] The central claim that domain shift has a nuanced role and that annealed likelihood schedules mitigate forward-model mismatch rests on measurements from a single physical phantom whose geometry and material properties closely mirror the synthetic Shepp-Logan used for training. Real clinical or industrial CT data exhibit far greater anatomical variability, polychromatic beam hardening, detector-specific noise, and scatter that are absent here. This limitation is load-bearing for the generalization of the reported mitigation strategy and the conclusion that performance gains do not translate.
  2. [Results] The abstract and summary describe qualitative observations of model collapse, hallucinations, and artifact mitigation, yet the provided description contains no quantitative metrics, error bars, or statistical tests comparing diverse versus narrow priors or the effect of annealed schedules. Without these, the robustness of the cross-condition claims cannot be verified.
minor comments (2)
  1. [Methods] Clarify the precise implementation of the annealed likelihood weight schedule and its integration into the Decomposed Diffusion Sampling algorithm, including any hyper-parameter choices.
  2. [Figures] Add scale bars, quantitative error maps, and direct side-by-side comparisons in the reconstruction figures to support visual claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps clarify the scope and presentation of our work on diffusion priors for experimental sparse-view CT. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Experimental Setup and Results] The central claim that domain shift has a nuanced role and that annealed likelihood schedules mitigate forward-model mismatch rests on measurements from a single physical phantom whose geometry and material properties closely mirror the synthetic Shepp-Logan used for training. Real clinical or industrial CT data exhibit far greater anatomical variability, polychromatic beam hardening, detector-specific noise, and scatter that are absent here. This limitation is load-bearing for the generalization of the reported mitigation strategy and the conclusion that performance gains do not translate.

    Authors: We agree that reliance on a single physical phantom closely matching the synthetic Shepp-Logan constitutes a genuine limitation for broad generalization claims. Our experimental design deliberately used this controlled phantom to isolate domain shift and forward-model mismatch effects that are otherwise confounded in heterogeneous clinical data. In the revised manuscript we will expand the Discussion and Limitations sections to explicitly state this constraint, qualify that the annealed-schedule mitigation is demonstrated under these specific conditions, and add a forward-looking paragraph on the need for validation against datasets with beam hardening, scatter, and anatomical variability. revision: yes

  2. Referee: [Results] The abstract and summary describe qualitative observations of model collapse, hallucinations, and artifact mitigation, yet the provided description contains no quantitative metrics, error bars, or statistical tests comparing diverse versus narrow priors or the effect of annealed schedules. Without these, the robustness of the cross-condition claims cannot be verified.

    Authors: We concur that quantitative support would strengthen the reported observations. Although the present manuscript emphasizes visual evidence to illustrate phenomena such as collapse and artifact reduction, we will add quantitative metrics (e.g., PSNR, SSIM) computed on the reconstructed volumes, include error bars derived from multiple independent sampling runs, and report simple statistical comparisons between prior diversity levels and likelihood schedules in the revised Results section. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in experimental pipeline

full rationale

The paper describes an empirical workflow: acquiring real CT measurements from a physical phantom, generating synthetic training sets with controlled domain shifts, and applying diffusion priors via an existing sampling scheme to sparse-view data. All reported outcomes (model collapse under severe mismatch, benefits of diverse priors, and mitigation via annealed schedules) are direct observations from these measurements and controlled variations. No mathematical derivation, fitted parameter, or self-citation is shown to define or force the central claims by construction; the results remain falsifiable against the physical data and are not equivalent to the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central findings rest on the representativeness of the physical phantom and the adequacy of the synthetic training variations to capture real-world mismatch; no new physical entities or large numbers of free parameters are introduced.

axioms (1)
  • domain assumption The physical phantom data and the chosen synthetic image sets adequately capture the domain shift and forward-model mismatch present in practical CT applications.
    This premise is invoked when the authors interpret their experimental results as general lessons for real-world deployment.

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Reference graph

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