Pith. sign in

REVIEW 3 major objections 5 minor 3 cited by

cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A conditional wavelet diffusion model synthesizes a missing brain MRI modality from the other three at full 3D resolution, reporting PSNR 28.72 and SSIM 0.946 on the challenge validation set.

desk verdict A competent, incremental conditional wavelet diffusion method whose headline clinical claim outstrips its evaluation; worth refereeing as a conditional accept. read the letter →

arxiv 2411.17203 v1 pith:VTI7QV3V submitted 2024-11-26 eess.IV cs.CV

Paul Friedrich , Alicia Durrer , Julia Wolleb , Philippe C. Cattin This is my paper · ORCID
classification eess.IVcs.CV
keywords conditionaldiffusionmodelswavelettransformcross-modalityimagesynthesis3DmedicalimagingmissingmodalitybrainMRIimage-to-imagetranslationtumorsegmentation
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 addresses a practical bottleneck in clinical tumor segmentation: the standard four MR contrasts (T1, T1ce, T2, FLAIR) are sometimes incomplete, and when one sequence is missing, pre-trained four-modality segmentation models cannot be applied directly. The authors treat the recovery of the missing volume as a paired image-to-image translation problem and solve it with cWDM, a wavelet diffusion model that operates on full-resolution 3D volumes instead of slices or patches. Conditioning is achieved by concatenating the wavelet coefficients of the three available modalities into every denoising step. On the 2024 brain MR image synthesis challenge validation set, the synthetic volumes reach MSE $1.82\times10^{-3}$, PSNR $28.72$, and SSIM $0.946$ when the missing modality is chosen at random. If these results hold, the method would let clinical imaging pipelines tolerate a missing sequence while still running standard tumor segmentation models.

What carries the argument

The central object is the conditional wavelet diffusion model. Its design replaces the learned autoencoder of latent diffusion with the discrete wavelet transform, a training-free operation that maps each $155\times240\times240$ volume to eight coefficient sub-bands at half spatial resolution. Conditioning is implemented by concatenating the wavelet coefficients of the three input modalities (24 channels) with the noisy coefficients of the target modality (8 channels) along the channel dimension, giving the denoising network a 32-channel input at every timestep. Sampling follows the DDPM reverse process and ends with the inverse wavelet transform. This lets the model process whole volumes directly, avoiding the inter-slice and inter-patch inconsistencies of slice- or patch-based synthesis.

What would settle it

Take a pre-trained four-modality brain tumor segmentation model, compute its Dice scores on real T1/T1ce/T2/FLAIR volumes, then replace one real modality with the cWDM-synthesized volume and recompute Dice; if tumor-region Dice drops substantially relative to the real-input baseline, the paper's claim that the method enables well-performing segmentation would be refuted.

Watch

Extended reading notes

Core claim

The central claim is that cross-modality 3D medical image synthesis can be solved directly on full-resolution volumes by combining a wavelet diffusion model with a simple conditioning strategy. The discrete wavelet transform maps each volume into eight half-resolution coefficient volumes, and the denoising network is fed the noisy wavelet coefficients of the target concatenated with the wavelet coefficients of the three conditioning volumes at every denoising step. Four models are trained, one for each missing modality, and they report pixel-level metrics on the challenge validation set. The paper states that these results suggest the method effectively addresses missing MR images and enables the application of well-performing segmentation models in clinical settings, although the segmentation evaluation itself is not reported.

Load-bearing premise

The load-bearing premise is that high pixel-level fidelity (MSE, PSNR, SSIM) on synthesized volumes carries over to the accuracy of downstream tumor segmentation models, a link the paper itself does not test.

Editorial extensions

If this is right

  • Full-resolution generation avoids inter-slice and inter-patch inconsistencies that appear when synthesis is done on slices or patches and then stitched.
  • Four models, one per missing modality, cover every case of one missing MR sequence among T1, T1ce, T2, and FLAIR.
  • When one modality is missing at random, the method's synthetic volumes attain MSE $1.82\times10^{-3}$, PSNR $28.72$, and SSIM $0.946$ on the challenge validation set.
  • The trained models can act as a preprocessing step that feeds pre-trained four-modality segmentation models, which is the stated clinical use case.
  • The same conditional wavelet diffusion setup transfers to other paired image-to-image translation problems such as CT$\leftrightarrow$MR and MR$\leftrightarrow$PET.

Reading between the lines

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

  • A single unified model conditioned on a one-hot modality indicator could replace the four separately trained models; the authors note this is feasible but expect a slight quality loss, so the multiplicity is a design choice rather than a requirement.
  • The decisive test of the clinical claim is a segmentation experiment: rerun a pre-trained four-modality tumor segmentation pipeline with the synthetic volume in place of the missing sequence and measure Dice against the real-volume baseline.
  • Because conditioning is just channel-wise concatenation in wavelet space, the scheme should extend to other missing-input settings, such as synthesizing a contrast-enhanced scan from non-contrast scans or completing a volume when two sequences are missing, though quality would presumably degrade with less conditioning information.
Share X Bluesky LinkedIn Reddit HN

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. This paper presents cWDM, a conditional wavelet diffusion model for 3D cross-modality brain MR synthesis, submitted as a contribution to the BraTS 2024 Brain MR Image Synthesis Challenge. The method generates a missing modality (T1, T1ce, T2, or FLAIR) conditioned on the three available modalities by concatenating the wavelet coefficients of the conditioning volumes to the noisy target coefficients at every denoising step of a wavelet diffusion model, so that generation runs on full-resolution volumes (155×240×240). Four models are trained separately, one per target modality. On the 219-case validation set, the authors report MSE 1.82e-3, PSNR 28.72, and SSIM 0.946 for a pseudo-random missing-modality protocol. The stated motivation is clinical: enabling downstream tumor segmentation when one sequence is missing. No segmentation evaluation is reported: Sec. 4.2 states that downstream segmentation scores could not be obtained for the validation set, and Sec. 4.3 postpones both image-quality and segmentation metrics on the 570-case test set to a future revision. The conclusion nevertheless asserts that the method enables the application of well-performing segmentation models in clinical settings.

Significance. The contribution is an empirical engineering result with a reproducible protocol: public code, four well-specified training runs, and an ablation study over skip connections, beta-schedules, and base channels. Full-resolution 3D generation is a genuine practical advantage because it avoids the stitching artifacts of slice- or patch-wise synthesis, and the concatenative conditioning scheme is simple and general enough to transfer to other paired translation tasks. If the missing links are closed (a downstream segmentation evaluation showing Dice parity between cWDM-supplemented and fully real inputs, and a comparison with at least one strong baseline), the method would be a useful contribution to the BraTS synthesis literature. As it stands, the evidence supports only pixel-level fidelity of the synthesized volumes rather than the clinically framed conclusion. The paper's strengths are its honest limitation statements, its public code, and the falsifiability of its pending test-set claims; its main weakness is the gap between the evidence reported and the scope of the conclusions drawn.

major comments (3)
  1. [Sec. 5; Sec. 4.2 (Tab. 1); Sec. 4.3] The central claim in the Conclusion — that the reported results 'suggest that our method effectively addresses the issue of missing MR images and enables the application of well-performing segmentation models in clinical settings' — is not supported by the evidence reported in the paper. Tab. 1 contains only pixel-level metrics (MSE 1.82e-3, PSNR 28.72, SSIM 0.946 on the pseudo-random validation set). These metrics do not establish that a segmentation model receiving three real volumes plus one cWDM-synthesized volume performs as well as on four real volumes; voxel-level fidelity is a weak proxy for task utility, since errors localized at tumor borders or enhancement rims can degrade segmentation while leaving SSIM and PSNR largely intact. The paper itself acknowledges the gap: Sec. 4.2 states that downstream segmentation scores could not be reported because the challenge did not provide masks or a scoring route for the validation set, and Sec. 4.3 defers such scores to the non-public test set. Because the training set of 1251 cases includes ground-truth masks (Sec. 4.1), a concrete test is available within the manuscript's scope: train a standard segmentation model (e.g., nnU-Net) on the real four-modality training volumes, then evaluate it on held-out cases with each modality in turn replaced by cWDM output, and compare Dice coefficients per tumor subregion against the same model given four real volumes. Either this experiment should be added or the conclusion should be limited to the image-fidelity evidence actually obtained.
  2. [Sec. 4.2 (Tab. 1); Sec. 4.4 (Tab. 2)] The quantitative evaluation contains no comparison against any existing method or baseline, so the claim that the method 'effectively addresses' missing-modality synthesis is made in a vacuum. Tab. 2 is an internal ablation over design choices (skip connection type, beta-schedule, base channels), and Tab. 1 reports only absolute scores of the chosen configuration. A reader cannot assess whether a simpler alternative — a slice-wise or patch-wise conditional diffusion baseline, a GAN-based 3D translator, or even a direct substitution baseline (e.g., using T2 where FLAIR is missing) — would give comparable fidelity at lower cost. The related work section (Sec. 1) cites several competing approaches, but none is evaluated. The paper should include at least one strong baseline, preferably the official BraTS 2024 challenge baseline when it becomes available, evaluated under identical preprocessing, validation protocol, and cropping, and also a patch- or slice-based variant of the proposed model to substantiate the claimed advantage of full-resolution processing.
  3. [Sec. 4.3] Sec. 4.3 states that quantitative evaluation scores (image quality and segmentation metrics) on the non-public test set containing 570 cases will be added 'as soon as they are provided by the challenge organizers.' As submitted, the empirical evaluation is therefore explicitly incomplete, and the missing test-set segmentation metrics are exactly the evidence that would substantiate the clinical framing in the abstract and conclusion. A journal submission should not rely on results to be supplied after review; the authors should either complete the test-set evaluation before resubmission or restructure the paper so that all claims are fully supported by experiments completed at submission time.
minor comments (5)
  1. [Sec. 4.1] The training description (1.2M iterations, batch size 1, Adam with learning rate 1e-5) does not state how the reported checkpoint was selected; the authors should specify the model-selection criterion (e.g., validation MSE over the final checkpoints) so that the reported metrics are not influenced by selection on the test set.
  2. [Sec. 4.4 (Tab. 2) and Sec. 4.2 (Tab. 1)] The ablation scores in Tab. 2 are computed on cropped volumes of resolution 155x224x224 ('to reduce the influence of black background voxels'), while the main results in Tab. 1 are computed on complete volumes of 155x240x240; the paper should state explicitly that values in the two tables are not directly comparable, or evaluate the ablation configurations on the same full volumes used for Tab. 1.
  3. [Tab. 1; Tab. 2] All reported metrics are means over the validation cases with no measure of variability; given the 219-case validation set, reporting standard deviations (or per-case box plots) for MSE, PSNR, and SSIM would materially strengthen the quantitative claims and is standard practice in the synthesis literature.
  4. [Sec. 4.4 (Tab. 2)] The selection of the linear schedule over the cosine schedule at C=64 is not fully transparent: the cosine configuration achieves the lowest MSE (1.62e-3) while the chosen linear configuration achieves the highest PSNR (29.14); the composite criterion used to declare the linear setup 'best' should be stated explicitly.
  5. [Fig. 2; Sec. 3] The symbol ⊕ is used to denote channel concatenation of wavelet coefficients, although ⊕ conventionally denotes element-wise addition; the notation would be clearer if an explicit concatenation symbol were introduced when defining c = DWT(C1) ⊕ DWT(C2) ⊕ DWT(C3) in Sec. 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the core cWDM result is an externally evaluated empirical contribution.

full rationale

The paper's central claim is that cWDM synthesizes a missing MR modality conditioned on three available modalities, and the evaluation compares synthesized volumes against real volumes on the externally supplied BraTS 2024 validation set (Tab. 1). No parameter is fitted to that validation set, and the target modality is excluded from the model input at inference, so the MSE/PSNR/SSIM scores are not forced by construction. The method builds on the authors' prior Wavelet Diffusion Model [7], and several references are self-citations; however, the prior model is used as a publicly available backbone implementation rather than as an unverified premise, and the novel conditioning mechanism is tested on held-out data. The paper explicitly acknowledges that downstream segmentation results could not be reported on the validation set, which is a missing-evidence gap rather than a circular step: pixel-level fidelity is not here equated with segmentation utility by any equation or fitted parameter. The conclusion's claim about enabling segmentation is broader than the reported evidence, but overreach is a correctness/evidence concern, not circularity. No derivation in the paper reduces to its own inputs.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

No invented entities. Free parameters are standard architecture hyperparameters selected by ablation; the model weights themselves are fitted to training data, which is the intended learning procedure.

free parameters (5)
  • diffusion timesteps T = 1000
    Chosen from prior DDPM work; not fitted.
  • beta schedule bounds = linear between 1e-4 and 0.02
    Chosen by ablation; affects generation quality.
  • base channels C = 64
    Selected by ablation study (Table 2) as best trade-off.
  • skip connection type = concatenation
    Chosen by ablation over additive versus concatenation.
  • training iterations = 1.2M
    Chosen by practical budget; no convergence analysis.
assumptions (5)
  • standard math Standard DDPM theory (forward and reverse diffusion, ELBO) holds for wavelet coefficients.
    Relies on Ho et al., Nichol and Dhariwal, and WDM.
  • standard math The discrete wavelet transform provides a lossless or near-lossless representation and the inverse transform reconstructs the image.
    DWT with appropriate filters is invertible; used in WDM.
  • domain assumption BraTS 2024 dataset images are co-registered and correspond across modalities for each subject.
    Paired image-to-image translation requires aligned training pairs.
  • domain assumption Clipping intensity at the 0.1 percentiles and normalizing to [0,1] is a valid preprocessing that does not remove clinically relevant information.
    Preprocessing choice affects all metrics and downstream applicability.
  • domain assumption The pseudo-validation set generated by randomly dropping one modality per subject is representative of the missing-modality clinical scenario.
    The evaluation protocol inherits the challenge organizers' script.

how reviews work

0 comments
Cite this review

Pith. "Pith review of cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis." pith.science (2026). https://pith.science/paper/VTI7QV3V

@misc{pith2026241117203,
  author       = {Pith},
  title        = {Pith review of: cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VTI7QV3V}},
  note         = {Machine review of arXiv:2411.17203}
}
abstract

This paper contributes to the "BraTS 2024 Brain MR Image Synthesis Challenge" and presents a conditional Wavelet Diffusion Model (cWDM) for directly solving a paired image-to-image translation task on high-resolution volumes. While deep learning-based brain tumor segmentation models have demonstrated clear clinical utility, they typically require MR scans from various modalities (T1, T1ce, T2, FLAIR) as input. However, due to time constraints or imaging artifacts, some of these modalities may be missing, hindering the application of well-performing segmentation algorithms in clinical routine. To address this issue, we propose a method that synthesizes one missing modality image conditioned on three available images, enabling the application of downstream segmentation models. We treat this paired image-to-image translation task as a conditional generation problem and solve it by combining a Wavelet Diffusion Model for high-resolution 3D image synthesis with a simple conditioning strategy. This approach allows us to directly apply our model to full-resolution volumes, avoiding artifacts caused by slice- or patch-wise data processing. While this work focuses on a specific application, the presented method can be applied to all kinds of paired image-to-image translation problems, such as CT $\leftrightarrow$ MR and MR $\leftrightarrow$ PET translation, or mask-conditioned anatomically guided image generation.

Figures

Figures reproduced from arXiv: 2411.17203 by the authors.

Figure 1
Figure 1. Schematic overview of the proposed pipeline for missing MR image generation - in this case, for a missing FLAIR image. We aim to generate the missing modality image conditioned on the three available images, ultimately allowing for pre-trained downstream task segmentation models to be applied. The same principle applies if another imaging modality is missing. For simplicity, all 3D volumes are displayed as 2D slices… view at source ↗
Figure 2
Figure 2. Schematic overview of the proposed conditional Wavelet Diffusion Model - in this case for a missing T1ce image. The process of generating the wavelet coefficients x˜0 of the output images is conditioned on the wavelet coefficients of the conditioning images by concatenating them with the noisy coefficients in each denoising step. learns to generate the denoised wavelet coefficients x˜0 of the target domain image giv… view at source ↗
Figure 3
Figure 3. Qualitative results of our proposed method. The synthetic images are generated conditioned on the real images from the three other modalities. We display the middle slice in the axial (top), sagittal (middle), and coronal (bottom) plane [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Additional qualitative results of our proposed method. The synthetic images are generated conditioned on the real images from the three other modalities. We display the middle slice in the axial (top), sagittal (middle), and coronal (bottom) plane [PITH_FULL_IMAGE:fig…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

    eess.IV 2026-08 conditional novelty 6.0 of 10

    A three-axis taxonomy (knowledge type, integration paradigm, architecture) for knowledge-guided 3D CT generation maps 25 methods and identifies geometric-mask-conditioned latent diffusion as the dominant paradigm.

  2. Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Wavelet-domain phase injection with low-frequency randomization improves realism and semantic consistency of sim-to-real translation, improving VLM planner ADE and FDE by about 5% on CARLA videos.

  3. K-Syn: K-space Data Synthesis in Ultra Low-data Regimes

    cs.CV 2025-09 conditional novelty 5.0 of 10

    K-Syn synthesizes realistic cardiac k-space data from very few training samples by combining frequency-domain feature learning with temporal-fusion guidance in a latent diffusion model.

Reference graph

Works this paper leans on

29 extracted references · 13 canonical work pages · cited by 3 Pith papers

  1. [1]

    Scientific data4(1), 1–13 (2017)

    Bakas, S., et al.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data4(1), 1–13 (2017)

    2017

  2. [2]

    arXiv preprint arXiv:1811.02629 (2018)

    Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

    arXiv 2018

  3. [3]

    In: Medical Imaging with Deep Learning (2023)

    Bieder, F., Wolleb, J., Durrer, A., Sandkuehler, R., Cattin, P.C.: Memory-efficient 3d denoising diffusion models for medical image processing. In: Medical Imaging with Deep Learning (2023)

    work page 2023

  4. [4]

    Advances in neural information processing systems34, 8780–8794 (2021)

    Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems34, 8780–8794 (2021)

    2021

  5. [5]

    arXiv preprint arXiv:2303.08189 (2023)

    Durrer, A., et al.: Diffusion models for contrast harmonization of magnetic reso- nance images. arXiv preprint arXiv:2303.08189 (2023)

    arXiv 2023

  6. [6]

    arXiv preprint arXiv:2410.17664 (2024)

    Friedrich, P., Frisch, Y., Cattin, P.C.: Deep generative models for 3d medical image synthesis. arXiv preprint arXiv:2410.17664 (2024)

    arXiv 2024

  7. [7]

    In: MICCAI Workshop on Deep Generative Models

    Friedrich, P., Wolleb, J., Bieder, F., Durrer, A., Cattin, P.C.: Wdm: 3d wavelet dif- fusion models for high-resolution medical image synthesis. In: MICCAI Workshop on Deep Generative Models. pp. 11–21. Springer (2024)

    work page 2024

  8. [8]

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

    Friedrich, P., Wolleb, J., Bieder, F., Thieringer, F.M., Cattin, P.C.: Point cloud diffusion models for automatic implant generation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 112–122. Springer (2023)

    work page 2023

Show all 29 references
  1. [9]

    European Radiology Experimental7(1), 70 (2023)

    Graf, R., et al.: Denoising diffusion-based mri to ct image translation enables au- tomated spinal segmentation. European Radiology Experimental7(1), 70 (2023)

    2023

  2. [10]

    Advances in neural information processing systems35, 478–491 (2022)

    Guth, F., Coste, S., De Bortoli, V., Mallat, S.: Wavelet score-based generative modeling. Advances in neural information processing systems35, 478–491 (2022)

    2022

  3. [11]

    Advances in neural information processing systems33, 6840–6851 (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems33, 6840–6851 (2020)

    2020

  4. [12]

    IEEE Transactions on Medical Imaging41(1), 145–157 (2021)

    Hu, S., et al.: Bidirectional mapping generative adversarial networks for brain mr to pet synthesis. IEEE Transactions on Medical Imaging41(1), 145–157 (2021)

    2021

  5. [13]

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

    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with condi- tional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017)

    2017

  6. [14]

    Scientific Reports13(1), 10568 (2023)

    Kalantar, R., et al.: Non-contrast ct synthesis using patch-based cycle-consistent generative adversarial network (cycle-gan) for radiomics and deep learning in the era of covid-19. Scientific Reports13(1), 10568 (2023)

    2023

  7. [15]

    Nature machine intelligence5(7), 799–810 (2023)

    Karargyris, A., et al.: Federated benchmarking of medical artificial intelligence with medperf. Nature machine intelligence5(7), 799–810 (2023)

    2023

  8. [16]

    In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

    Kim, J., Park, H.: Adaptive latent diffusion model for 3d medical image to image translation: Multi-modal magnetic resonance imaging study. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 7604–7613 (2024)

    2024

  9. [17]

    Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Diverse image-to- imagetranslationviadisentangledrepresentations.In:ProceedingsoftheEuropean conference on computer vision (ECCV). pp. 35–51 (2018)

    2018

  10. [18]

    arXiv preprint arXiv:2305.09011 (2023) cWDM for Cross-Modality 3D Medical Image Synthesis 11

    Li, H.B., et al.: The brain tumor segmentation (brats) challenge 2023: Brain mr image synthesis for tumor segmentation (brasyn). arXiv preprint arXiv:2305.09011 (2023) cWDM for Cross-Modality 3D Medical Image Synthesis 11

    2023

  11. [19]

    IEEE transactions on medical imaging34(10), 1993–2024 (2014)

    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging34(10), 1993–2024 (2014)

    2014

  12. [20]

    In: International conference on machine learning

    Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International conference on machine learning. pp. 8162–8171. PMLR (2021)

    2021

  13. [21]

    arXiv preprint arXiv:2305.00042 (2023)

    Pan, S., et al.: Cycle-guided denoising diffusion probability model for 3d cross- modality mri synthesis. arXiv preprint arXiv:2305.00042 (2023)

    2023 arXiv

  14. [22]

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

    Phung, H., Dao, Q., Tran, A.: Wavelet diffusion models are fast and scalable image generators. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10199–10208 (2023)

    2023

  15. [23]

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

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)

    2022

  16. [24]

    In: ACM SIGGRAPH 2022 conference proceedings

    Saharia, C., et al.: Palette: Image-to-image diffusion models. In: ACM SIGGRAPH 2022 conference proceedings. pp. 1–10 (2022)

    2022

  17. [25]

    In: International conference on machine learning

    Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsuper- vised learning using nonequilibrium thermodynamics. In: International conference on machine learning. pp. 2256–2265. PMLR (2015)

    2015

  18. [26]

    Computerized Medical Imaging and Graphics 86, 101801 (2020)

    Uzunova, H., Ehrhardt, J., Handels, H.: Memory-efficient gan-based domain trans- lation of high resolution 3d medical images. Computerized Medical Imaging and Graphics 86, 101801 (2020)

    2020

  19. [27]

    In: 2021 40th Chinese Control Conference (CCC)

    Zhao, P., Pan, H., Xia, S.: Mri-trans-gan: 3d mri cross-modality translation. In: 2021 40th Chinese Control Conference (CCC). pp. 7229–7234. IEEE (2021)

    2021

  20. [28]

    In: Proceedings of the IEEE interna- tional conference on computer vision

    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE interna- tional conference on computer vision. pp. 2223–2232 (2017)

    2017

  21. [29]

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

    Zhu, L., et al.: Make-a-volume: Leveraging latent diffusion models for cross- modality 3d brain mri synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 592–601. Springer (2023)

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.