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REVIEW 3 major objections 4 minor 14 references

Modality Translation and Registration of MR and Ultrasound Images Using Diffusion Models

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read By translating both MR and ultrasound into a boundary-focused pseudo-modality, this paper makes prostate image registration more accurate than translating one modality into the other.

desk verdict A plausible incremental extension of the authors' PMT method that improves MR-US registration metrics, but the learned intermediate modality is self-referential and the evaluation lacks uncertainty quantification. read the letter →

arxiv 2506.01025 v1 pith:SFZ2AY7F submitted 2025-06-01 cs.CV

Xudong Ma , Nantheera Anantrasirichai , Stefanos Bolomytis , Alin Achim This is my paper
classification cs.CV
keywords MR-USregistrationprostatecancermodalitytranslationdiffusionmodelSchrödingerbridgeintermediatepseudo-modalityfeaturedisentanglement
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 proposes a way to make MR-to-ultrasound registration for prostate cancer easier by first translating both images into a shared intermediate pseudo-modality that keeps anatomical boundaries and drops texture details. The authors argue that the usual approach—translating one modality into the other—leaves irrelevant detail that misleads the registration, whereas their anatomically coherent modality translation (ACMT) suppresses that detail. On their internal dataset, ACMT improves modality-similarity scores (FID and KID) over the compared baselines and produces the best downstream registration, with average surface distance 6.82 versus 9.18 for the runner-up. The claim is that a customized boundary-focused intermediate domain is enough to make cross-modal deformable registration both more accurate and more anatomically plausible.

What carries the argument

The central object is the intermediate pseudo-modality $P_1$, a boundary-focused image domain that is hypothesized to be a common destination for both MR and US. The mechanism that carries the argument is a Schrödinger Bridge diffusion model with Conditional Flow Matching, which defines a family of Gaussian conditional distributions along a path from source distribution $P_0$ to target $P_1$ and lets the network be trained to map any intermediate state to the terminal target. The network's hierarchical feature disentanglement loss is what pushes $P_1$ to have the right content: a shallow-layer texture consistency term with a larger kernel matches texture between the translated MR and US, and a deep-layer boundary preservation term with a smaller kernel plus Sobel filtering keeps anatomical edges. The Schrödinger Bridge loss ties the whole translation to the optimal-transport path.

What would settle it

A concrete test: on a dataset with manual landmark annotations, measure target registration error after registering original MR-US images versus ACMT-translated images with the same registration method; if boundary-only translation does not reduce landmark error below original-image registration, the central claim fails. Simpler: if translating both modalities to the same boundary-focused target collapses distinct anatomical structures that are visible in one modality, the intermediate-domain hypothesis is refuted.

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Extended reading notes

Core claim

The paper's central discovery is that both MR and ultrasound images of the prostate can be translated—using a Schrödinger Bridge diffusion model—into a single learned intermediate modality that retains boundary information while suppressing texture, and that this translation improves a downstream deformable registration compared with registering the original images or images translated by two existing methods. The network is trained with a hierarchical feature disentanglement loss: shallow features, processed by a 7x7 convolution, enforce texture consistency between translated MR and US, while deep features, processed by a 3x3 convolution and a Sobel filter, preserve anatomical boundaries. A Schrödinger Bridge loss keeps the translation along the optimal-transport path. In the reported experiments, ACMT attains the lowest FID (138.01) and KID (0.09) among the translation methods, and in registration it attains DSC 0.95, IoU 0.90, and ASD 6.82—the best DSC and ASD of the compared setups, with ASD 25.7% lower than the runner-up.

Load-bearing premise

Everything rests on the hypothesis, stated in Section II-B, that an ideal intermediate modality exists that contains mostly boundaries and no texture, and that both MR and US can be mapped to it; the paper defines this target with losses rather than proving it exists independently.

Editorial extensions

If this is right

  • If ACMT is correct, the best route to MR-US registration may be a two-stage pipeline: translate both images into the boundary-focused pseudo-modality, then register within that domain, rather than trying to map one modality directly onto the other.
  • Registration can be improved without synthesizing photorealistic textures; suppressing modality-specific detail is a feature, not a failure, of translation for registration.
  • The reported 25.7% reduction in average surface distance over the runner-up indicates that boundary preservation, not just texture matching, is the main driver of the gain.
  • Because the method is unsupervised, it could be retrained on new MR-US pairs without segmentation labels, which matters for clinical deployment where annotated data are scarce.

Reading between the lines

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

  • If the intermediate-boundary hypothesis generalizes, the same translate-both-to-boundary recipe could be tried for other multimodal registration problems, such as CT-MR or PET-CT, where anatomies share boundaries but not texture.
  • The paper's evidence does not directly show that a single ideal intermediate modality exists independently of the losses; one testable extension is to compare ACMT's translated images against explicit edge maps or segmentations to see whether boundaries alone can support the same registration gains.
  • FID and KID measure distribution similarity, not anatomical correctness; a sharper test would be landmark-based target registration error on an external annotated dataset.
  • Since the registration evaluator is fixed, the claim is about translation quality relative to that registrar; a natural extension is to test whether boundary-focused translation also improves other deformable registration methods.
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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 / 4 minor

Summary. The paper proposes ACMT, an unsupervised modality-translation method that uses a Schrödinger-bridge diffusion model to translate both MR and ultrasound prostate images into a learned 'intermediate pseudo-modality' P1. The translation is supervised by three losses: a texture-consistency loss on shallow features, a boundary-preservation loss on deep Sobel-filtered features, and a Schrödinger-bridge consistency loss. The translated images are then fed into a fixed registration model, FSDiffReg, and the paper reports that ACMT achieves the best FID/KID among UNSB, PMT, and original images, and the best DSC/ASD in downstream registration, with a 25.7% ASD improvement over PMT. The central claim is that translating both modalities into a boundary-focused intermediate representation removes registration-irrelevant texture and improves deformable alignment.

Significance. If the reported results are statistically robust, the idea of translating both MR and US toward a common boundary-oriented intermediate domain is a useful contribution to multimodal registration, and the comparison under a fixed registration backbone is a reasonable evaluation design. The paper also provides a concrete, self-contained method with quantitative comparisons to two baselines and visual examples. However, the current evidence does not yet establish the central interpretability claim: the intermediate modality is defined only through the network's own outputs, FID/KID are reported without a defined reference distribution, and the registration improvements are presented without error bars, significance tests, or a stated number of patients. The method is not reproducible from the text because the loss weights and other hyperparameters are not reported. These issues are load-bearing for the paper's claim that the translated images are 'anatomically coherent' and that ACMT 'effectively removes irrelevant information'.

major comments (3)
  1. [Section II-B/II-C, Eqs. (4), (7)-(13)] The intermediate modality P1 is never defined by an external reference set or anatomical ground truth; it exists only as the terminal state of the network being trained. In the training loop of Section II-C, each noisy state xt_i is generated by Eq. (4), which requires a terminal state x1, and the only available x1 is the network's own prediction fθ(...). The texture loss (Eq. 7) aligns shallow features of the two translated outputs to each other, the boundary loss (Eqs. 8-9) forces each translated output's Sobel-filtered deep features to match its own source, and the SB loss (Eqs. 11-13) compares xt_i with the network's own prediction. All three losses can be satisfied by collapsing both outputs to a shared low-entropy image, such as a common blurred boundary map, without any constraint that this image corresponds to real anatomy. Consequently, the reported FID/KID and ASD improvements may reflect image simplification and self-consistency rather than the hypothesized 'anatomically coherent' intermediate domain. The paper should provide independent validation of P1, for example by comparing translated boundary maps to manual anatomical segmentations or by evaluating registration on landmark-annotated cases, and should state explicitly what external evidence, if any, supports the existence of P1.
  2. [Section III-B, Table I] The FID and KID values in Table I are reported without specifying the reference image set. For a standard modality-translation evaluation, FID/KID compare generated images to real images from the target domain; here the target is the hypothesized P1, for which no real reference images exist. If the FID is computed between translated MR and translated US images, or between translated images and some subset of original images, the 'decrease by 65.91%' has a different meaning in each case. The paper must state exactly which image sets were used to compute FID and KID, how many images each set contained, and why those sets are appropriate for evaluating similarity to an intermediate modality that has no ground-truth samples.
  3. [Section III-B, Table II] The registration evaluation lacks statistical support. The paper does not report the number of patients or test cases, the number of frames manually segmented per case, or any variance measure such as standard deviation or confidence interval for DSC, IoU, or ASD. The 'several key frames' annotation protocol means that DSC/IoU/ASD are computed on a sparse set of 2D slices, which may not faithfully represent volumetric 3D registration accuracy. Without error bars or a paired significance test, the reported 25.7% ASD improvement over PMT cannot be distinguished from sampling noise. The authors should report the full test-set size, per-case results, mean and standard deviation, and a paired statistical test (e.g., Wilcoxon signed-rank) for each metric.
minor comments (4)
  1. [Section II-D, Eq. (14)] The loss weights λ_texture, λ_boundary, and λ_SB are described as 'carefully tuned' but their values are not given, and no sensitivity analysis or ablation is included. Reporting these values is necessary for reproducibility.
  2. [Section II-D, Eq. (11)] In Eqs. (11)-(12), the notation x1 = fθ(x1 | x_ti) is circular because x1 appears on both sides. It should read x1 = fθ(x_ti) or otherwise clarify that x1 on the left is the network prediction conditioned on x_ti.
  3. [Section II-B, 'Diffusion Process via CFM formulation'] The sentence 'This diffusion process applies of our modality translation network' is incomplete and should be rewritten. There are also several other grammatical issues in Sections I and II that should be corrected.
  4. [Section IV, Conclusion] The text 'SOAT methods' should be 'SOTA methods'.

Circularity Check

2 steps flagged · score 4.0 of 10

The intermediate pseudo-modality P1 is self-referential: the Schrödinger Bridge diffusion states are generated from the network's own predicted x1, and the reported FID/KID gains largely restate the texture-consistency loss; only the registration evaluation against manual masks is independent.

  1. self definitional [Sec. II-B 'Diffusion Process via CFM formulation' and Sec. II-C Step 2; Eq. (4) and Eqs. (11)-(12)]
    "we hypothesize the existence of an ideal intermediate modality that focuses primarily on boundary information while disregarding texture details. ... using the network in evaluation mode (with all parameters frozen): generate xti through the diffusion process as described in the purple block of Fig. 1 ... xtj+1 = wtj+1 x1 + (1− wtj+1 )xtj + N (0, αj+1I) ... where xM R 1 = fθi (xM R 1 |xM R ti ) and xU S 1 = fθi (xU S 1 |xU S ti ) are the terminal states predicted by the network."

    The target distribution P1 is never defined by external data, anatomical ground truth, or any fixed reference set. The only terminal state x1 available to run the diffusion process in Eq. (4) is the network's own prediction fθ(...). The training inputs xti are therefore generated from the network's current output, and the SB loss in Eqs. (11)-(12) measures distance between xti and the same network's prediction. Thus the 'ideal intermediate modality' is defined by the very network being trained: any output satisfying the losses is declared to be P1. The claimed removal of irrelevant information and preservation of anatomical boundaries in P1 is therefore not independently falsifiable from the translation objective itself; only the downstream registration metrics provide external evidence.

  2. renaming known result [Sec. III 'Evaluation' and Table I vs. Eq. (7)]
    "To assess the quality of modality translation, we employed two widely-used metrics: FID and KID. ... Ltexture = ∥C7×7(Fs,M R 1 ) − C7×7(Fs,U S 1 )∥2 2 , (7)"

    The paper reports FID/KID improvements in Table I as evidence of superior modality translation, but the intermediate modality P1 has no real reference images. The only computable distribution-distance comparison is between the two translated outputs, and Ltexture in Eq. (7) is exactly a feature-space distance between those two outputs that is minimized during training. Consequently, the FID/KID drop is partly a restatement of the training objective rather than an independent verification of 'anatomically coherent' translation. The registration DSC/IoU/ASD scores against manual masks are the truly independent results, and they do not rescue the translation-quality claim from this circularity.

full rationale

The paper's downstream registration claim is not circular: it is evaluated with the same FSDiffReg registration model across methods, and prostate masks are manually segmented, so the DSC/IoU/ASD improvements are externally anchored. That limits the score to 4 rather than 6+. However, the central 'anatomically coherent intermediate modality' premise is self-referential. The diffusion process used to create training states requires a terminal x1, and the only x1 available is the network's own prediction; no fixed P1 samples or anatomical ground truth enter the objective. The texture-consistency, boundary-preservation, and SB losses therefore define P1 as whatever the network produces, rather than measuring against an independently existing target. The FID/KID 'modality similarity' gains are also largely a direct consequence of Ltexture, since no real P1 exists as a reference. The registration evidence shows that the learned simplification helps alignment on this dataset, but the paper's stronger narrative that it captures the hypothesized ideal anatomical intermediate domain is not independently established and partially reduces to the training losses by construction.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The method depends on several hand-set loss weights, an unreported noise scale, and a hypothesized intermediate domain with no external target. The three losses define the target, so the translation-quality metrics are partially self-referential; only the registration metrics against manual masks provide independent grounding.

free parameters (5)
  • lambda_texture = not reported
    Weight for L_texture in Eq. 14; described as 'carefully tuned' with no value given, and it controls the balance between texture consistency and boundary/SB terms.
  • lambda_boundary = not reported
    Weight for L_boundary in Eq. 14; no value reported.
  • lambda_SB = not reported
    Weight for L_SB in Eq. 14; no value reported.
  • diffusion noise scale sigma = not reported
    Appears in Eqs. 2-4 and 11-12 as the Wiener-measure variance; controls bridge stochasticity and is not specified.
  • time step pool and number of steps T = not reported
    Training picks t_i from a predefined pool (Sec. II-C); schedule and size are not specified.
assumptions (4)
  • ad hoc to paper A single ideal intermediate modality P1 exists and can be reached from both MR and US while retaining only boundary information and discarding texture details.
    Stated as a hypothesis in Sec. II-B ('we hypothesize the existence of an ideal intermediate modality...'). The entire loss design and evaluation assume this target without independent evidence.
  • domain assumption Shallow CNN features encode texture and deep features encode boundaries, so large kernels on shallow features and small kernels on deep features achieve the desired disentanglement.
    Invoked in Sec. II-D, citing [8]; this is a common but not guaranteed property of CNNs and is not verified for this specific model.
  • domain assumption The Schrödinger Bridge / conditional flow matching formulation provides a valid generative path between distributions and can be optimized with the stated losses.
    Borrowed from [4], [5], [11], [12]; the paper uses these results as background theory without proof.
  • ad hoc to paper FID and KID computed on this private test set are meaningful measures of modality similarity when no real intermediate-domain reference images exist.
    The paper reports FID/KID in Table I without stating the reference set; standard FID/KID require a target distribution, but no real P1 samples are available.
invented entities (1)
  • Intermediate pseudo-modality P1
    purpose: A constructed target domain that both MR and US images are translated toward, intended to carry registration-relevant boundaries and suppress texture details.
    P1 is hypothesized in Sec. II-B and defined only through the network's losses. No independent samples or external measurements of this domain are provided, so there is no falsifiable handle outside the paper.

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Cite this review

Pith. "Pith review of Modality Translation and Registration of MR and Ultrasound Images Using Diffusion Models." pith.science (2026). https://pith.science/paper/SFZ2AY7F

@misc{pith2026250601025,
  author       = {Pith},
  title        = {Pith review of: Modality Translation and Registration of MR and Ultrasound Images Using Diffusion Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SFZ2AY7F}},
  note         = {Machine review of arXiv:2506.01025}
}
read the original abstract

Multimodal MR-US registration is critical for prostate cancer diagnosis. However, this task remains challenging due to significant modality discrepancies. Existing methods often fail to align critical boundaries while being overly sensitive to irrelevant details. To address this, we propose an anatomically coherent modality translation (ACMT) network based on a hierarchical feature disentanglement design. We leverage shallow-layer features for texture consistency and deep-layer features for boundary preservation. Unlike conventional modality translation methods that convert one modality into another, our ACMT introduces the customized design of an intermediate pseudo modality. Both MR and US images are translated toward this intermediate domain, effectively addressing the bottlenecks faced by traditional translation methods in the downstream registration task. Experiments demonstrate that our method mitigates modality-specific discrepancies while preserving crucial anatomical boundaries for accurate registration. Quantitative evaluations show superior modality similarity compared to state-of-the-art modality translation methods. Furthermore, downstream registration experiments confirm that our translated images achieve the best alignment performance, highlighting the robustness of our framework for multi-modal prostate image registration.

Figures

Figures reproduced from arXiv: 2506.01025 by the authors.

Figure 1
Figure 1. Hierarchical Feature Disentanglement framework based on a Diffusion Model [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Modality translation results for two patients (two rows), showing original US and MR images, UNSB translation, and [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Registration results for two patients. Each row corresponds to one patient and displays the original US and MR images, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

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Works this paper leans on

14 extracted references · 13 canonical work pages

  1. [1]

    : Population-based organised prostate cancer testing: results from the first invitation of 50-year-old men

    Bratt, O., et al. : Population-based organised prostate cancer testing: results from the first invitation of 50-year-old men. European Urology 85(3), 207–214 (2024)

    work page 2024

  2. [2]

    Medical physics 48(6), 3074–3083 (2021)

    Chen, Y ., Xing, L., Yu, L., Liu, W., Pooya Fahimian, B., Niedermayr, T., Bagshaw, H.P., Buyyounouski, M., Han, B.: Mr to ultrasound image registration with segmentation-based learning for hdr prostate brachytherapy. Medical physics 48(6), 3074–3083 (2021)

    work page 2021

  3. [3]

    Artificial Intelligence Review 56(1), 615– 651 (2023)

    Jiang, J., Guo, Y ., Bi, Z., Huang, Z., Yu, G., Wang, J.: Segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms. Artificial Intelligence Review 56(1), 615– 651 (2023)

    work page 2023

  4. [4]

    In: ICLR (2024)

    Kim, B., Kwon, G., Kim, K., Ye, J.C.: Unpaired image-to-image translation via neural schr ¨odinger bridge. In: ICLR (2024)

    work page 2024

  5. [5]

    Dynamical Systems 34(4), 1533– 1574 (2014)

    L ´eonard, C.: A survey of the schrodinger problem and some of its connections with optimal transport. Dynamical Systems 34(4), 1533– 1574 (2014)

    work page 2014

  6. [6]

    In: Annual Conference on Medical Image Understanding and Analysis

    Ma, X., Anantrasirichai, N., Bolomytis, S., Achim, A.: Pmt: Partial- modality translation based on diffusion models for prostate magnetic resonance and ultrasound image registration. In: Annual Conference on Medical Image Understanding and Analysis. pp. 285–297. Springer (2024)

    work page 2024

  7. [7]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Preechakul, K., Chatthee, N., Wizadwongsa, S., Suwajanakorn, S.: Dif- fusion autoencoders: Toward a meaningful and decodable representation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10619–10629 (2022)

    work page 2022

  8. [8]

    International Journal of Robotics and Control Systems 2(4), 739–748 (2022)

    Purwono, P., Ma’arif, A., Rahmaniar, W., Fathurrahman, H.I.K., Frisky, A.Z.K., ul Haq, Q.M.: Understanding of convolutional neural network (cnn): A review. International Journal of Robotics and Control Systems 2(4), 739–748 (2022)

    work page 2022

Show all 14 references
  1. [9]

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

    Qin, Y ., Li, X.: Fsdiffreg: Feature-wise and score-wise diffusion-guided unsupervised deformable image registration for cardiac images. In: International Conference on Medical Image Computing and Computer- Assisted Intervention. pp. 655–665. Springer (2023)

    2023

  2. [10]

    Wireless Personal Communications 132(1), 651–677 (2023)

    Ranjan, R., Avasthi, V .: Edge detection using guided sobel image filtering. Wireless Personal Communications 132(1), 651–677 (2023)

    2023

  3. [11]

    : Conditional flow matching: Simulation-free dynamic optimal transport

    Tong, A., et al. : Conditional flow matching: Simulation-free dynamic optimal transport. arXiv preprint arXiv:2302.00482 2(3) (2023)

    2023 arXiv

  4. [12]

    : Deep generative learning via schr ¨odinger bridge

    Wang, G., et al. : Deep generative learning via schr ¨odinger bridge. In: International conference on machine learning. pp. 10794–10804. PMLR (2021)

    2021

  5. [13]

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

    Yang, X., Wang, X.: Diffusion model as representation learner. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 18938–18949 (2023)

    2023

  6. [14]

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

    Youwang, K., Oh, T.H., Pons-Moll, G.: Paint-it: Text-to-texture synthesis via deep convolutional texture map optimization and physically-based rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4347–4356 (2024)

    2024

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