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REVIEW 3 major objections 5 minor 33 references

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read First unsupervised CT metal artifact reduction matches supervised models on synthetic data and outperforms them on clinical scans.

desk verdict A credible unsupervised MAR method with a clear disentanglement formulation, but the clinical generalization claim rests on qualitative images and an additivity assumption the paper itself concedes may transfer lesion-like anatomy. read the letter →

arxiv 1908.01104 v4 pith:BI3GYISD submitted 2019-08-03 eess.IV cs.CV

Haofu Liao , Wei-An Lin , S. Kevin Zhou , Jiebo Luo This is my paper · ORCID
classification eess.IVcs.CV
keywords metalartifactreductionunsupervisedlearningdisentanglementcomputedtomographyimage-to-imagetranslationgenerativeadversarialnetworkslatentspaceCTartifacts
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

CT scans of patients with metal implants are marred by streaking and shading artifacts that obscure anatomy. Existing deep-learning fixes train on paired images synthesized to mimic the artifacts, but synthetic artifacts do not match real clinical physics, so the models transfer poorly. This paper claims the first unsupervised alternative: a network that separates each image into a content code and an artifact code in latent space, using only unlabeled groups of artifact-affected and artifact-free CT images. With artifact reduction, transfer, and self-reconstruction losses, it removes streaks without paired data. On a synthesized test set it roughly matches supervised baselines, and on two clinical datasets it generalizes better than the supervised models it is compared with.

What carries the argument

The load-bearing object is the artifact consistency loss, Eq. (8): $\mathcal{L}_{\mathrm{art}} = \mathbb{E}[\|(x_a - \hat{x}) - (\hat{y}_a - y)\|_1]$, which forces the artifact subtracted from an artifact-affected image to equal the artifact added to an artifact-free image under the same artifact code. This is what lets the network learn anatomical precision without paired images. It is supported by an artifact pyramid decoding design that merges the artifact code into the content decoder at multiple scales, and by the full set of competing losses (adversarial, reconstruction, self-reduction) that keep the separated codes meaningful.

What would settle it

Take a set of clinical metal-affected CT images and their metal-free counterparts reconstructed from the same patient (e.g., before and after implant removal); run ADN trained only on unpaired images and check in metal-free tissue whether the difference between the network's output and the true clean image is near zero, and if the artifact code from one anatomy reproduces the true artifact when applied to a different anatomy.

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Extended reading notes

Core claim

The paper's central claim is that metal artifact reduction can be learned without any anatomically paired artifact-free ground truth by treating it as artifact disentanglement. The artifact disentanglement network (ADN) maps an artifact-affected image through a content encoder and an artifact encoder into separate latent spaces, and an artifact-free image through its own content encoder. Artifact removal is decoding the content code alone; artifact synthesis is decoding a content code together with an artifact code. Four losses drive the separation: adversarial losses make each output look like its target domain, a reconstruction loss keeps the autoencoding faithful, an artifact consistency loss requires the artifact stripped from an artifact image to equal the artifact added to a clean image, and a self-reduction loss requires that removing the synthesized artifact from a transferred image recovers the clean original. The paper reports that on synthesized data ADN is on par with supervised methods, while on clinical scans—where supervised models trained on synthetic artifacts degrade—ADN removes streaks and shading without introducing secondary artifacts.

Load-bearing premise

The method assumes that the metal artifact in an image is an additive component that can be lifted out and re-attached to another image unchanged; if real artifacts depend on the underlying anatomy, the network may erase anatomical structures instead of artifacts.

Editorial extensions

If this is right

  • Clinics could reduce metal artifacts without needing paired scans or access to raw projection data, since ADN works directly on reconstructed CT images.
  • Supervised MAR models trained on synthetic artifacts fail under domain shift; an unsupervised model trained on the target domain's own unpaired images is a more robust route, as demonstrated by the clinical results.
  • The same architecture should transfer to other artifact types (noise, streaks, blur) because the formulation does not assume a specific artifact mechanism.
  • Artifact synthesis from one patient applied to another's artifact-free image could supply realistic metal-affected training data for downstream tasks like segmentation.
  • The self-reduction loop provides a general recipe for adding self-supervision to image translation when paired data is unavailable.

Reading between the lines

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

  • The artifact consistency loss is the likeliest failure point: if real metal artifacts are non-additive and anatomy-dependent (beam hardening, scatter), the equality in Eq. (8) can be satisfied by transferring anatomy-dependent residue into the artifact code, letting the network delete real structures.
  • A testable extension: apply ADN to a dataset with known non-linear artifacts and compare the artifact code's transferability across anatomies; if the code carries anatomy-specific content, artifact transfer quality will drop when source and target anatomies differ greatly.
  • Another extension: measure whether the model's clinical generalization comes from the unsupervised objective or from the absence of synthetic-data bias; training a supervised model directly on clinical artifact-affected images with noisy pseudo-labels could isolate the cause.
  • If artifact synthesis is realistic enough, it could serve as data augmentation for supervised models, which would be a practical bridge between the two paradigms.
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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 / 5 minor

Summary. The paper proposes ADN, an unsupervised metal artifact reduction (MAR) method for CT images. The method is based on disentangling an artifact code and a content code in latent space, using specialized encoders/decoders and a combination of adversarial, reconstruction, artifact consistency, and self-reduction losses to avoid the need for paired training data. The authors evaluate ADN on one synthesized dataset (SYN) with quantitative PSNR/SSIM and on two clinical datasets (CL1, CL2) with qualitative comparisons. They report that ADN outperforms existing unsupervised baselines on SYN, attains performance comparable to supervised models, and generalizes better to clinical data than supervised models. The source code is released publicly.

Significance. If the claims hold, this would be the first unsupervised MAR method that avoids paired synthetic training and may generalize better to clinical data. The architecture is well motivated, and the ablation study (M1-M4) shows that each loss component contributes meaningfully to the quantitative results on SYN. The public implementation is a strength for reproducibility. The central limitation is that the key clinical-generalization claim rests on qualitative evaluation alone, and the artifact consistency loss embodies a potentially unjustified additivity assumption that could cause loss of anatomical content.

major comments (3)
  1. [Sec. IV-E and abstract] The abstract and Sec. IV-E claim that ADN shows 'better generalization ability over the supervised models' on clinical datasets. This claim is supported only by qualitative visual comparisons; there is no quantitative metric, reader study, blinded evaluation, or statistical test on CL1 or CL2. For a clinical MAR method, the more important claim is that the method removes artifacts without erasing anatomical structures. Some form of quantitative content-preservation check (e.g., lesion/landmark preservation, or comparison of anatomical contours against a reference such as NMAR) is needed to support the generalization claim. Without it, the abstract's central claim is overstated.
  2. [Eq. (8), Sec. III-B] The artifact consistency loss Lart = E[||(xa - xhat) - (yhat_a - y)||_1] enforces that the residual removed from an artifact-affected image equals the residual added to an artifact-free image. This assumes that the artifact component is additive and independent of the underlying anatomy. The physical mechanisms of metal artifacts (beam hardening, scatter, partial volume) cited in Sec. I do not guarantee such additivity. The paper's own caveat in Sec. IV-G, that an anatomical structure or lesion that looks like metal artifacts 'might also be transferred,' indicates that the artifact code can contain content-dependent information. This undermines the claim in Sec. IV-E that ADN removes dark shadings and streaks 'without introducing secondary artifacts' while preserving anatomy. The authors should either provide evidence that the artifact code does not carry anatomy-specific content (e.g., an experiment on paired data with inserted lesions) or explicitly discuss this as a limitation and potential failure mode.
  3. [Sec. IV-E, Figs. 7-8] The quantitative evaluation on SYN (Table II) is sound, but the clinical evaluation is purely qualitative without criteria, statistical analysis, or error bars. To justify the paper's contribution, the authors should provide a more rigorous clinical evaluation, for example a reader study with multiple evaluators and inter-rater agreement, or at least a quantitative proxy such as noise/streak metrics and anatomical-structure preservation scores. Alternatively, the claims in the abstract and Sec. I should be tempered to state that ADN shows promising qualitative generalization.
minor comments (5)
  1. [Sec. IV-E] Please ensure that the abstract's claim of 'better generalization ability' is consistent with the evaluation section, which itself states that only qualitative comparisons are performed on clinical data.
  2. [Table II] The MUNIT result (SSIM 7.5) is far lower than the other baselines; a brief note explaining that MUNIT prioritizes diverse outputs over structural fidelity would help the reader interpret this outlying value.
  3. [Sec. III-A] The notation for the artifact-affected encoder E_Ia as a set of two encoders (Ec_Ia and Ea_Ia) could be visually clarified in the text; the current superscript/subscript placement in the PDF is easy to misread.
  4. [References] References [18] and [24] both refer to CycleGAN, and [19] and [25] both refer to MUNIT; these duplicates should be consolidated.
  5. [Sec. III-C] Minor grammatical issue: 'the padding of all the convolutional layers in the blocks ... are reflection padding' should be 'is reflection padding.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ADN's self-supervision losses are consistency objectives trained on unpaired data, and the reported SYN metrics are measured on a held-out set against ground truth.

full rationale

The paper's central claim—unsupervised MAR via artifact disentanglement—does not reduce to its inputs by construction. The training objective (Eq. 5) combines adversarial losses, reconstruction (Eq. 7), artifact consistency (Eq. 8), and self-reduction (Eq. 9). Eq. 8 enforces that the residual (xa−x̂) matches the residual (ŷa−y); this is a self-supervised cycle assumption about artifact additivity, not a definitional identity that makes the artifact-removed output equal to an input or a fitted label. Eq. 9 likewise is a cycle-consistency regularizer, and the reported PSNR/SSIM on SYN are computed against ground-truth artifact-free test images withheld from training, so the numerical result is not forced by a fitted parameter. The preliminary-version self-citation [22] is disclosed rather than used as load-bearing justification, and no uniqueness theorem from the authors is invoked to rule out alternatives. The paper's own caveat in Sec. IV.G—that a lesion resembling metal artifacts might be transferred—is a limitation of the artifact-additivity assumption, not a circular step; it affects clinical reliability but does not make the derivation equivalent to its input. The clinical comparison is qualitative, which limits the strength of the generalization claim, but that is an evidence-quality concern outside the circularity definition.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on hand-set loss weights and dataset-splitting thresholds, plus domain assumptions about artifact additivity and the availability of artifact-group labels. No new physical entities are introduced.

free parameters (2)
  • Loss weights λadv, λrec, λself, λart = λadv=1.0; λrec=λself=λart=20.0 for SYN/CL1, 5.0 for CL2
    Hand-set hyperparameters in Eq. (5); no sensitivity analysis or validation sweep is reported, so their values partly determine the reported PSNR/SSIM.
  • CL1 group selection thresholds = HU > 2,500 and metal region > 400 pixels for artifact-affected; max HU < 2,000 for artifact-free
    Hand-chosen thresholds in Sec. IV-B; they define the two training domains and may bias the clinical evaluation.
assumptions (3)
  • domain assumption Any artifact-affected CT image can be decomposed into independent content and artifact components in latent space.
    Invoked in Sec. III (Fig. 1); the entire method rests on this separation, but unsupervised disentanglement is known to be underdetermined without inductive biases (the paper cites Locatello et al. [8]).
  • domain assumption The artifact code a satisfies (xa - x̂) ≈ (ŷa - y), i.e., the artifact is additive and independent of content.
    Used in Lart (Eq. 8) to enforce anatomical closeness; assumes metal artifacts behave like an additive layer, which physical models (beam hardening, scatter, partial volume) do not imply.
  • domain assumption Images can be reliably grouped into artifact-affected and artifact-free domains.
    Sec. IV-B uses HU thresholds to split CL1; for CL2, artifact-free CT images from CL1 are assumed to be a valid counterpart for CBCT images, a cross-modality assumption.

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Cite this review

Pith. "Pith review of ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction." pith.science (2026). https://pith.science/paper/BI3GYISD

@misc{pith2026190801104,
  author       = {Pith},
  title        = {Pith review of: ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BI3GYISD}},
  note         = {Machine review of arXiv:1908.01104}
}
read the original abstract

Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that disentangles the metal artifacts from CT images in the latent space. It supports different forms of generations (artifact reduction, artifact transfer, and self-reconstruction, etc.) with specialized loss functions to obviate the need for supervision with synthesized data. Extensive experiments show that when applied to a synthesized dataset, our method addresses metal artifacts significantly better than the existing unsupervised models designed for natural image-to-image translation problems, and achieves comparable performance to existing supervised models for MAR. When applied to clinical datasets, our method demonstrates better generalization ability over the supervised models. The source code of this paper is publicly available at https://github.com/liaohaofu/adn.

Figures

Figures reproduced from arXiv: 1908.01104 by the authors.

Figure 1
Figure 1. Artifact disentanglement. The content and artifact [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. An illustration of the relationships between the loss [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Basic building blocks of the encoders and decoders: [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Detailed architecture of the proposed artifact pyramid [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison with baseline methods on the [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison with baseline methods on the [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison of different variants of ADN. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Metal artifact transfer. Left: the clinical images with [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

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Reference graph

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