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REVIEW 2 major objections 48 references

Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction

T0 review · 2 major / 0 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read A coordinate network first extrapolates truncated CBCT data without ring artifacts, then a physics-based iterative module re-injects high-frequency textures from the original projections.

desk verdict The paper combines a self-supervised INR for artifact-free extrapolation in truncated CBCT with a follow-on iterative refinement step to recover textures, but the abstract gives no metrics or checks on whether that refinement actually stays stable. read the letter →

arxiv 2606.06039 v1 pith:RBAL5H6X submitted 2026-06-04 cs.CV

Genyuan Zhang , Junyao Wang , Haoran Lan , Chuandong Tan , Songtao Zhu , Fenglin Liu This is my paper
classification cs.CV
keywords conebeamCTtruncatedreconstructionimplicitneuralrepresentationartifactsuppressiontexturepreservationself-supervisedlearningiterativerefinementdataextrapolation
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 establishes a self-supervised reconstruction method for cone-beam CT when projections are truncated. It maps 3D coordinates directly to radiodensity values under projection supervision, which removes the need for filtering or backprojection steps and thereby avoids truncation artifacts while extending the field of view. Because pure coordinate networks lose high-frequency content, the framework adds an iterative refinement stage that starts from the network output and progressively transfers structural detail back from the measured projections. This hybrid design is shown to work on both simulated and real datasets without any paired full-volume ground truth.

What carries the argument

The implicit neural scene representation (a coordinate network that maps spatial coordinates to radiodensity) whose output initializes the physics-based iterative refinement module that injects projection-derived high-frequency information.

What would settle it

A controlled test on a physical phantom containing known high-frequency patterns where the final hybrid reconstruction shows lower texture fidelity or new artifacts compared with a standard iterative reconstruction started from filtered back-projection would falsify the claim that the neural initialization is optimal.

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Extended reading notes

Core claim

The central claim is that an implicit neural representation, trained only on truncated projections, produces an artifact-free extrapolated volume that serves as an optimal initialization; a subsequent physics-based iterative module then re-extracts and restores high-frequency structural information directly from the same projections, yielding a final volume that combines neural extrapolation with iterative texture fidelity.

Load-bearing premise

The neural coordinate network must generate an artifact-free extrapolated volume that functions as an optimal starting point so the iterative module can recover textures without introducing new artifacts or needing extra supervision.

Editorial extensions

If this is right

  • Truncation-induced ring artifacts are eliminated because the coordinate network bypasses conventional filtering and backprojection.
  • Continuous three-dimensional extrapolation beyond the measured field of view becomes possible without additional data.
  • High-frequency clinical textures are restored by direct projection supervision in the refinement stage.
  • Training requires no paired full-volume ground truth, only the available truncated projections.

Reading between the lines

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

  • The same two-stage pattern could be tested on other incomplete-data tomography problems such as limited-angle or sparse-view reconstruction.
  • If the neural initialization consistently outperforms conventional starts, detector size requirements in CBCT systems might be reduced in practice.
  • A natural next measurement would be to quantify how much the iterative stage improves specific texture metrics on clinical head or dental datasets.
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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.

Referee Report

2 major / 0 minor

Summary. The paper proposes a self-supervised framework for truncated CBCT reconstruction that uses a coordinate network (implicit neural representation) to map 3D spatial coordinates directly to radiodensity values under projection supervision. This is claimed to eliminate truncation-induced ring artifacts and enable continuous 3D extrapolation without traditional filtering or backprojection. A physics-based iterative refinement module is then added, taking the network's artifact-free extrapolated volume as initialization to progressively recover and inject high-frequency structural details from the original projections while avoiding new artifacts. Experiments on simulated and real datasets are asserted to demonstrate unification of neural artifact suppression with iterative detail preservation.

Significance. If the central claims hold with supporting evidence, the work would address an important practical limitation in CBCT imaging by providing a self-supervised alternative that avoids the need for ground-truth volumes and handles variable truncation. The combination of coordinate networks for low-frequency extrapolation with iterative refinement for texture recovery is a plausible direction, and the self-supervised projection loss is a methodological strength that could reduce reliance on paired training data.

major comments (2)
  1. [Abstract] Abstract: The unification claim rests on the assertion that the coordinate network produces an 'artifact-free extrapolated volume' that serves as an 'optimal initialization' allowing the iterative module to 'progressively re-extract and inject high-frequency structural information' without introducing new artifacts. No derivation, stability analysis, or ablation is described to show why standard iterative schemes (known to re-amplify truncation rings when emphasizing high frequencies) remain stable under this initialization or how the self-supervised loss alone enforces the required frequency separation.
  2. [Abstract] Abstract: The manuscript asserts successful performance on simulated and real-world datasets but provides no quantitative metrics (e.g., PSNR, SSIM, artifact indices), no ablation studies on the iterative module, and no comparison against baselines, making it impossible to evaluate whether the texture-preservation or artifact-suppression claims hold.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and outline planned revisions to strengthen the presentation of our contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The unification claim rests on the assertion that the coordinate network produces an 'artifact-free extrapolated volume' that serves as an 'optimal initialization' allowing the iterative module to 'progressively re-extract and inject high-frequency structural information' without introducing new artifacts. No derivation, stability analysis, or ablation is described to show why standard iterative schemes (known to re-amplify truncation rings when emphasizing high frequencies) remain stable under this initialization or how the self-supervised loss alone enforces the required frequency separation.

    Authors: We agree that the abstract is concise and omits explicit theoretical details on frequency separation and stability. The manuscript describes the coordinate network's spectral bias and the role of the physics-based iterative module, but does not provide a dedicated derivation or stability analysis. We will add a new subsection in the methods with a mathematical derivation of how the self-supervised projection loss promotes frequency separation, a stability analysis of the iterative refinement initialized by the neural representation, and an ablation study quantifying the effect of this initialization on ring artifact re-amplification. revision: yes

  2. Referee: [Abstract] Abstract: The manuscript asserts successful performance on simulated and real-world datasets but provides no quantitative metrics (e.g., PSNR, SSIM, artifact indices), no ablation studies on the iterative module, and no comparison against baselines, making it impossible to evaluate whether the texture-preservation or artifact-suppression claims hold.

    Authors: The current manuscript version does not include specific quantitative metrics, ablations on the iterative module, or baseline comparisons in the abstract or results sections. We will revise the abstract to summarize key quantitative outcomes and expand the experiments section to include PSNR, SSIM, and artifact index values, dedicated ablations isolating the iterative module, and comparisons against baselines such as FDK and other reconstruction methods on both simulated and real datasets. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation relies on external measured projections

full rationale

The paper's core chain maps coordinates to density under direct projection supervision from measured data, then feeds the resulting volume into a physics-based iterative module that re-extracts high frequencies from the same original projections. No equation or procedure is shown that defines a target quantity in terms of itself or renames a fitted parameter as a prediction; the artifact-free claim follows from bypassing filtering/backprojection rather than from any self-referential construction. No self-citation is invoked as load-bearing justification, and the supervision source remains external to the model's outputs.

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

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The framework implicitly relies on standard neural network optimization and the assumption that projection consistency can drive both extrapolation and texture recovery.

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Cite this review

Pith. "Pith review of Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction." pith.science (2026). https://pith.science/paper/RBAL5H6X

@misc{pith2026260606039,
  author       = {Pith},
  title        = {Pith review of: Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RBAL5H6X}},
  note         = {Machine review of arXiv:2606.06039}
}
read the original abstract

Cone-beam computed tomography (CBCT) frequently suffers from data truncation, which introduces severe artifacts and limits the effective field of view (FOV). Existing deep learning methods for truncated cone-beam computed tomography (CBCT) reconstruction suffer from serious limitations, including a strict reliance on supervised ground truth and a failure to account for continuous 3D spatial truncation variations. To address these challenges, we introduce a self-supervised 3D reconstruction framework based on neural scene representations. By directly mapping spatial coordinates to radiodensity under projection supervision, our approach inherently bypasses traditional filtering and backprojection operations, thereby fundamentally eliminating truncation-induced ring artifacts while enabling robust continuous 3D data extrapolation. However, coordinate networks are susceptible to an inherent spectral bias, which leads to a severe loss of clinically vital high-frequency textures. To resolve this bottleneck, we further incorporate a physics-based iterative refinement module into the neural scene representation architecture. Leveraging the artifact-free, extrapolated volume from the coordinate network as an optimal initialization, this module progressively re-extracts and injects high-frequency structural information from the original projections back into the volume. Extensive experiments on both simulated and real-world datasets demonstrate that our method successfully unifies the exceptional artifact suppression and extrapolation capabilities of neural networks with the high-fidelity detail preservation of iterative algorithms.

Figures

Figures reproduced from arXiv: 2606.06039 by the authors.

Figure 1
Figure 1. (a) Representative reconstruction results based on the NeRF method [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Field of view analysis: (a) 3D view; (b) Main view; (c) Top view [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The overall pipeline of our method. In (a) neural scene representations, a conical beam X-ray scan obtains the measured truncated projection, and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Visual evaluation results for the pancreatic dataset (FOV: approx. 125 pixels, 56th axial slice). The red box delineates the ROI; the bottom-left corner [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Reconstruction results in the coronal and sagittal planes using different methods. The bottom right corner displays a magnified view of the red ROI [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Compare the improvement effects of different algorithms under the proposed iterative architecture (170th axial slice). The second line shows a [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Results of the visual assessment of the horizontal plane in the real-world experiment (199th axial slice). The magnified view of the ROI is located in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Results of the sagittal-plane visual assessment from the real-world experiment. An enlarged view of the ROI is located in the bottom right corner. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: PSNR and SSIM change curves with the number of iterations of [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: High-frequency information outside the field of view (FOV) is [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: Visual evaluation results for the pelvis dataset (FOV: approx. 150 pixels, 80th axial slice). The red box delineates the ROI; the bottom-left corner [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Visual evaluation results for the abdomen dataset (FOV: approx. 203 pixels, 230th axial slice). The red box delineates the ROI; the bottom-left corner [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]

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