A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging

Cheng Li; Hao Yang; Jiarun Liu; Shanshan Wang; Shoujun Yu; Tao Tan; Xuanru Zhou

REVIEW 4 major objections 7 minor 25 references

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read TSSC-Net claims that conditioning a diffusion model on the start and end frames of a 4D MRI scan generates all intermediate frames (6x temporal super-resolution in a single step), and a tri-directional Mamba post-processor restores…

desk verdict A plausible two-stage diffusion+Mamba pipeline for 4D MRI interpolation with real gains on large-motion knee data, but the diffusion target notation needs correction before the 6x interpolation claim is trustworthy. read the letter →

arxiv 2506.04116 v2 pith:J4AHFDFR submitted 2025-06-04 eess.IV cs.AIcs.CV

Xuanru Zhou , Jiarun Liu , Shoujun Yu , Hao Yang , Cheng Li , Tao Tan , Shanshan Wang This is my paper

classification eess.IVcs.AIcs.CV

keywords 4DMRItemporalsuper-resolutiondiffusionmodelspatialconsistencyMambaframeinterpolationcross-frameattention

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

TSSC-Net claims that a two-stage deep learning pipeline can turn just two MRI frames into a complete 12-frame 4D dynamic sequence, achieving 6x temporal super-resolution in a single inference step while correcting the cross-slice inconsistencies that arise when frames are generated slice by slice. The paper argues this matters because registration-based interpolation, the standard way to synthesize intermediate frames, fails on large-amplitude motion. On an in-house 12-case knee dataset with flexion from 0 to 80 degrees, TSSC-Net reports PSNR 20.041 dB and SSIM 0.730 versus 17.580 dB and 0.635 for the best registration-based baseline. On the public ACDC cardiac dataset the method is competitive, with PSNR 32.971 dB and SSIM 0.977, showing that the gains are largest when motion is fast and large.

What carries the argument

The load-bearing object is the two-stage pipeline. Stage 1 is a transformer-based diffusion model whose reverse process is conditioned on the start frame $I_0$ and end frame $I_1$; cross-frame attention lets the denoiser use both boundary frames to synthesize intermediate frames, and DDIM acceleration is used at inference. Stage 2 is a spatial consistency enhancement network built from residual tri-directional Mamba blocks, each scanning the 3D volume along three orderings of the x, y, and z axes (SSM$_{xyz}$, SSM$_{yzx}$, SSM$_{zxy}$) to capture long-range dependencies in all directions. It is trained with a composite loss combining MSE, wavelet transform, and total variation regularization. The crucial work is that Stage 1 supplies temporally plausible but slice-wise inconsistent volumes, and Stage 2 acts as a learned 3D post-processor restoring volumetric coherence.

What would settle it

Train Stage 2 on arbitrarily corrupted volumes rather than Stage 1 outputs, or test Stage 2 on a new motion pattern such as a different joint or a larger flexion range, and measure whether the PSNR and SSIM gains over the w/o SC ablation persist; if the gains drop to near zero when Stage 1's artifact distribution shifts, the spatial-consistency correction is memorization rather than general volumetric coherence.

Watch

Extended reading notes

Core claim

The central claim is that a diffusion model conditioned on the start and end frames of a 4D MRI scan can generate all intermediate frames, providing 6x temporal super-resolution in a single inference step, and that a second network using residual tri-directional Mamba blocks can repair the cross-slice inconsistencies created by generating slices independently. The paper reports that on fast, large-motion knee MRI this beats the registration-based baselines by a wide margin, while on low-motion cardiac MRI it matches the best baseline. The ablation study without the spatial consistency network drops PSNR by about 3.6 dB on cardiac and 1.9 dB on knee, which the paper attributes to the spatial consistency network correcting cross-slice misalignment.

Load-bearing premise

The whole two-stage claim rests on Stage 2 learning to correct general cross-slice inconsistencies rather than memorizing the specific artifacts Stage 1 produced during training, but the paper never specifies how Stage 2's training pairs were constructed.

Editorial extensions

If this is right

A complete 12-frame 4D dynamic sequence can be produced from only two acquired frames, potentially reducing the scan-time burden of 4D MRI.
The method should be most clinically valuable where motion is fast and large, such as knee flexion or cardiac phases with large deformation, since the reported gains over registration-based baselines are largest there.
The two-stage design implies that temporal coherence and spatial coherence can be treated separately: diffusion handles the temporal gap, and tri-directional Mamba handles cross-slice consistency.
On low-motion data the method is competitive but not clearly superior, so the claimed advantage is specifically for large-motion scenarios.
Cross-slice errors from independently generated slices are correctable by a learned 3D refinement network, according to the ablation study.

Reading between the lines

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

An implication the authors leave implicit is that the same architecture could be applied to other dynamic volumes with sparse temporal sampling, such as 4D CT or fetal MRI, as long as boundary frames bracket a coherent motion.
The 6x single-step interpolation claim suggests testing on larger temporal leaps, such as 8x or 12x, to see whether cross-frame attention degrades gracefully; the paper does not report such scaling.
If Stage 2 generalizes as a true consistency corrector, it could be attached to any slice-wise generation method, not just this diffusion Stage 1; this is a testable extension the paper does not make.
The reported knee gains come from only 12 scans, so the practical magnitude for clinical use depends on whether the gap holds on a larger multi-center cohort, which the paper itself leaves for future work.

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.

Desk Editor's Note

read the letter

Two things worth knowing. The framework is a two-stage pipeline: a diffusion model interpolates 2D slices from start/end frames, then a residual tri-directional Mamba network cleans up cross-slice inconsistencies in the reassembled 3D volume. The second stage is the more interesting piece, and the knee results are real: 20.04 dB PSNR versus 17.58 for TransMorph, with the ablation dropping to 18.13 without the spatial consistency module. On ACDC, TSSC-Net essentially ties UVI-Net, so the large-motion knee dataset is the evidence that matters.

What is genuinely new: the combination of diffusion-based temporal interpolation with a Mamba module that scans along three orthogonal axes for volumetric coherence. I have not seen that specific pairing in the cited literature, and the ablation supports that the tri-directional scan contributes something.

The soft spots. First, the diffusion formulation in Section 2.2 is wrong as written. Eq. (1) sets x0 = I1, and Eq. (2) conditions on I0 and I1 for every reverse step. Literally, that means the model is reconstructing one of its own conditioning inputs, not producing an intermediate frame. The intended reading is presumably that x0 is an intermediate frame and I0 and I1 are the boundary conditions, but a referee cannot tell from the text. This is the load-bearing issue and it must be fixed.

Second, the 'single inference step' wording conflicts with DDIM acceleration, which normally uses multiple sampling steps. The authors should state exactly how many steps they run.

Third, no diffusion-based baseline is included, even though LDMVFI and VDT are cited. That leaves the central comparison to registration-based methods and one unsupervised flow approach.

Fourth, the Stage 2 training procedure is underspecified. How are the input volumes to the spatial consistency network constructed during training? If they are Stage 1 outputs, fine, but that is not said; if they are ground-truth volumes, then the post-processor is being trained on a different distribution from what it sees at inference.

Fifth, the knee dataset is 12 in-house cases, private, with no significance tests, and the code is not released despite the abstract promising it. That is a lot to rest on.

For peer review: I would send it out, with a firm request to rewrite the diffusion equations, report sampling steps, add a diffusion baseline, specify Stage 2 pairing, and make at least some data or code available. The core idea is worth testing, but the current manuscript is not self-contained enough to verify the central claim.

Referee Report

4 major / 7 minor

Summary. This paper presents TSSC-Net, a two-stage framework for 4D MRI temporal super-resolution. Stage 1 uses a diffusion model conditioned on start and end frames to generate intermediate 2Dt frames, and Stage 2 uses residual tri-directional Mamba blocks to improve 3D spatial consistency. The method is evaluated on the ACDC cardiac MRI dataset and an in-house dynamic knee dataset, with PSNR/SSIM/MAE comparisons against three baselines (VM, TM, UVI-Net) and an ablation that removes Stage 2. The central claims are 6x temporal super-resolution (12 frames from 2 input frames) in a single inference step, and improved performance on large-motion knee imaging (PSNR 20.041 dB vs. 17.580 dB for TM).

Significance. The application is timely, and the reported results on the knee dataset are promising: the PSNR gain of about 2.5 dB over TM and SSIM of 0.730 vs. 0.635 suggest that the method may handle large inter-frame motion better than registration-based interpolation. The manuscript also includes an ablation study, qualitative comparisons, and a commitment to release code, which are strengths. However, the diffusion formulation in Stage 1 is internally inconsistent as written, so the central interpolation claim is not yet established. The paper needs clarification of the generation target, conditioning variables, and inference protocol before its contributions can be assessed.

major comments (4)

[Section 2.2, Eqs. (1)-(2)] The forward process is described as being applied to the target frame I1 with x0 = I1, but the reverse process in Eq. (2) conditions on I0 and I1, i.e., on the very frame being generated. Taken literally, Stage 1 reconstructs a conditioning frame rather than synthesizing intermediate frames, which would invalidate the claimed 6x temporal super-resolution. The authors must state explicitly which frame is noised, which frames are used as conditioning, and how the 10 intermediate frames are produced (e.g., one denoising trajectory per intermediate frame, or a single trajectory that outputs all frames). As written, the method is indistinguishable from conditional autoencoding of an input frame.
[Section 2.2 and Section 3.2] The repeated claim of "6x temporal super-resolution in a single inference step" is inconsistent with the described iterative DDIM sampling: the reverse diffusion process requires multiple denoising steps. The authors need to define the inference procedure: how many DDIM steps are used, whether each output frame is sampled independently, and whether "single inference step" means one model forward pass per frame or one complete sampling trajectory. Without this, the computational claim and the temporal-interpolation mechanism remain ambiguous.
[Section 2.3 and Section 3.2] The training data for the spatial consistency enhancement network is not specified. The loss in Eq. (5) is defined on the output of the network, but the paper does not state how input-output pairs are constructed: are the inputs the Stage-1 generated volumes and the targets the ground-truth volumes? If Stage 2 is trained only on the artifacts of Stage 1 on the training set, the ablation in Table 2 may reflect memorization of those specific artifact patterns rather than a generalizable correction capability. The authors should describe the pairing procedure and any data augmentation, and ideally evaluate Stage 2 on held-out volumes.
[Section 3.3, Table 1] The evaluation protocol is under-specified: it is not stated whether the reported PSNR/SSIM are computed on all 12 frames of the generated sequence, only the 10 interpolated frames, or the 2 input frames plus 10 generated frames. This matters because the compared baselines (VM, TM, UVI-Net) are registration-based methods whose outputs may be evaluated on the same frame set. Please specify the exact frames used for evaluation and the preprocessing applied to the in-house knee dataset.

minor comments (7)

[Section 2.2, Eq. (2)] The text uses "epsilon_t" for the fixed variance, but Eq. (2) writes sigma_t^2 I; please make the notation consistent.
[Section 2.2] There is a typo: "de-sign" should be "design".
[Section 3.2] The paper lists "PyTorch 11.7"; this is not a standard PyTorch version. It should be "PyTorch 1.11" or "PyTorch 2.x".
[Section 2.1 and Section 2.2] The terms "2Dt" and "3D" are used without definition; please define them at first use to clarify that Stage 1 operates on 2D slices across time and Stage 2 on 3D volumes.
[Fig. 1 caption] The labels "Pred 3Dt" and "Pred 3D0" in Fig. 1 are unclear; please explain what t and 0 denote.
[Abstract] The sentence "To solve temporal fidelity under fast motion" should be "To improve temporal fidelity under fast motion".
[Abstract] The code URL is split across lines in the abstract; please format it as a single hyperlink.

Circularity Check

1 steps flagged · score 8.0 of 10

Stage 1's diffusion target x0=I1 is also the conditioning frame I1 in Eq. (2), so the claimed 6x temporal super-resolution reduces by construction to reconstructing an input frame.

self definitional [Section 2.2, Equations (1)-(2), Diffusion-driven temporal super-resolution]
"In TSSC-Net, this process is applied to the target frame I1. Starting from x0 = I1, the forward process generates a sequence x1, x2, . . . , xT. ... The reverse diffusion process ... At each reverse step t, the goal is to sample xt−1 from the conditional distribution. pθ(xt−1|xt, I0, I1) = N(xt−1; µθ(xt, t, I0, I1), σt^2I)"

The forward process defines the clean target as x0 = I1, while the reverse process conditions on I0 and I1, i.e., on the very frame being generated. The model is therefore trained to denoise a corrupted version of the end-frame input while receiving that same end frame as a conditioning signal. Any sampled output approximates I1, not an intermediate frame. No intermediate frame is ever defined as x0 in the forward process, so the paper's central claim of generating a complete 12-frame sequence from two frames collapses: the asserted output variable is, by construction, one of the two inputs. The '6x temporal super-resolution' prediction reduces to conditional reconstruction of an input frame.

full rationale

The only reduction-by-construction I can quote is in Stage 1: the forward process is applied to 'the target frame I1' with x0=I1, and the reverse process conditions on I0 and I1. Because the target frame is also a conditioning frame, the diffusion output is a reconstruction of an input rather than a newly generated intermediate frame. This makes the paper's headline '6x temporal super-resolution from two input frames' self-definitional: the asserted output variable is defined to be one of the inputs. The rest of the paper is not circular: benchmark comparisons against VM, TM, and UVI-Net use external baselines and public ACDC data, and the Stage 2 spatial-consistency network is a learned post-processor trained with MSE/wavelet/TV losses on ground-truth volumes; its training-pair construction is underspecified, but that is a completeness concern, not a circularity. There is no load-bearing self-citation chain or imported uniqueness theorem. If the x0=I1 statement were a typo for an intermediate frame, the manuscript never states which frame is noised, how ten intermediate frames are produced, or how they are paired in training, so the circularity stands on the written equations.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper's central claim rests on standard diffusion and Mamba machinery. The only hand-tuned free parameter is the set of loss weights (all set to 1). The two-stage pipeline relies on domain assumptions about the effectiveness of post-hoc 3D correction, but no new physical objects are invented.

free parameters (1)

Loss weighting factors (lambda_MSE, lambda_WT, lambda_TV) = 1, 1, 1
Set to 1 'based on experimental validation' (Section 2.3, Eq. 5), i.e., tuned by the authors without a reported ablation.

assumptions (3)

standard math Standard DDPM forward and reverse processes as defined in Ho et al. (Eq. 1 and 2)
The paper uses the standard Markov chain diffusion definition; no proof is provided, which is acceptable.
domain assumption Mamba state space model update (Eq. 3 and 4) works as a sequence model for 3D volumes
The paper assumes that flattening 3D volumes into sequences and applying the SSM update captures long-range spatial dependencies, but it does not specify the flattening order or how the three scans are aggregated.
domain assumption The two-stage pipeline (slice-wise generation then 3D correction) can reconstruct anatomically faithful volumes
The paper assumes that independently generated 2D slices plus a learned 3D post-processor yields coherent anatomy, without validating against anatomical plausibility or downstream tasks.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging." pith.science (2026). https://pith.science/paper/J4AHFDFR

@misc{pith2026250604116,
  author       = {Pith},
  title        = {Pith review of: A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J4AHFDFR}},
  note         = {Machine review of arXiv:2506.04116}
}

read the original abstract

In medical imaging, 4D MRI enables dynamic 3D visualization, yet the trade-off between spatial and temporal resolution requires prolonged scan time that can compromise temporal fidelity--especially during rapid, large-amplitude motion. Traditional approaches typically rely on registration-based interpolation to generate intermediate frames. However, these methods struggle with large deformations, resulting in misregistration, artifacts, and diminished spatial consistency. To address these challenges, we propose TSSC-Net, a novel framework that generates intermediate frames while preserving spatial consistency. To improve temporal fidelity under fast motion, our diffusion-based temporal super-resolution network generates intermediate frames using the start and end frames as key references, achieving 6x temporal super-resolution in a single inference step. Additionally, we introduce a novel tri-directional Mamba-based module that leverages long-range contextual information to effectively resolve spatial inconsistencies arising from cross-slice misalignment, thereby enhancing volumetric coherence and correcting cross-slice errors. Extensive experiments were performed on the public ACDC cardiac MRI dataset and a real-world dynamic 4D knee joint dataset. The results demonstrate that TSSC-Net can generate high-resolution dynamic MRI from fast-motion data while preserving structural fidelity and spatial consistency.

Figures

Figures reproduced from arXiv: 2506.04116 by the authors.

**Figure 1.** The overall framework of the proposed method. The framework operates in two stages. In Stage 1, diffusion-driven temporal super-resolution is performed on 2Dt data to generate intermediate frames. In Stage 2, spatial consistency enhancement is applied to the generated 3D volumes to ensure volumetric coherence. 2 Proposed Method 2.1 Framework Overview The overall framework of the proposed diffusion-driven temporal su… view at source ↗

**Figure 2.** Illustration of the three distinct selective scan orders in the residual tridirectional mamba blocks. Each order processes volumetric data along a different sequence of x, y, and z axes, effectively capturing multi-directional dependencies for enhanced spatial consistency. the temporal gap between key frames. Specifically, our approach conditions the diffusion process on the known start and end frames, I0 and I1. … view at source ↗

**Figure 3.** Qualitative comparison between synthesized cardiac (left) and knee (right) MRI sequences, displaying six evenly-spaced frames from each 12-frame series. Red arrows highlight regions where TSSC-Net better preserves anatomical details. dynamic knee MRI dataset (12 cases) was acquired using a United Imaging 3T scanner. The knee flexion angles ranged from 0° to 80°, and each 4D scan contained 12 frames of 3D MRI capture… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Qualitative comparisons of generated images from the ablation study. The red boxes indicate areas of noticeable improvement in structural coherence when the spatial consistency network is included. consistency enhancement leads to misalignment across adjacent slices. B…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

25 extracted references · 15 canonical work pages

[1]

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

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsuper- vised learning model for deformable medical image registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9252–9260 (2018)

work page 2018
[2]

IEEE transactions on medical imaging38(8), 1788–1800 (2019)

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE transactions on medical imaging38(8), 1788–1800 (2019)

2019
[3]

IEEE Transactions on Emerging Topics in Computational Intel- ligence (2024)

Baniya, A.A., Lee, T.K., Eklund, P.W., Aryal, S.: A survey of deep learning video super-resolution. IEEE Transactions on Emerging Topics in Computational Intel- ligence (2024)

work page 2024
[4]

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging37(11), 2514–2525 (2018)

2018
[5]

Medical image analysis82, 102615 (2022)

Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: Transmorph: Transformer for unsupervised medical image registration. Medical image analysis82, 102615 (2022)

2022
[6]

Behavior Research Methods, Instruments, & Computers25(2), 101–113 (1993)

Cohen, J.D., Noll, D.C., Schneider, W.: Functional magnetic resonance imaging: Overview and methods for psychological research. Behavior Research Methods, Instruments, & Computers25(2), 101–113 (1993)

work page 1993
[7]

In: Proceedings of the AAAI Conference on Artificial Intelligence

Danier, D., Zhang, F., Bull, D.: Ldmvfi: Video frame interpolation with latent diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 1472–1480 (2024)

work page 2024
[8]

Investigative radiology58(1), 60–75 (2023)

Eck, B.L., Yang, M., Elias, J.J., Winalski, C.S., Altahawi, F., Subhas, N., Li, X.: Quantitative mri for evaluation of musculoskeletal disease: cartilage and muscle composition, joint inflammation, and biomechanics in osteoarthritis. Investigative radiology58(1), 60–75 (2023)

work page 2023

Show all 25 references

[9]

Advances in neural infor- mation processing systems27(2014)

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural infor- mation processing systems27(2014)

2014
[10]

arXiv preprint arXiv:2312.00752 (2023)

Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)

2023 arXiv
[11]

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

Han, K., Xiong, Y., You, C., Khosravi, P., Sun, S., Yan, X., Duncan, J.S., Xie, X.: Medgen3d: A deep generative framework for paired 3d image and mask generation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 759–769. Springer ...

2023
[12]

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
[13]

Journal of pharmacokinetics and pharmacodynamics40, 281–300 (2013)

Ingrisch, M., Sourbron, S.: Tracer-kinetic modeling of dynamic contrast-enhanced mri and ct: a primer. Journal of pharmacokinetics and pharmacodynamics40, 281–300 (2013)

2013
[14]

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

Jeong, J., Kim, K.D., Nam, Y., Cho, K., Kang, J., Hong, G.S., Kim, N.: Generating high-resolution 3d ct with 12-bit depth using a diffusion model with adjacent slice and intensity calibration network. In: International Conference on Medical Image Computing and Computer-Assiste...

2023
[15]

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

Kim, B., Ye, J.C.: Diffusion deformable model for 4d temporal medical image gen- eration. In: International Conference on Medical Image Computing and Computer- Assisted Intervention. pp. 539–548. Springer (2022)

2022
[16]

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

Kim, J., Yoon, H., Park, G., Kim, K., Yang, E.: Data-efficient unsupervised inter- polation without any intermediate frame for 4d medical images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11353–11364 (2024)

2024
[17]

arXiv preprint arXiv:2305.13311 (2023)

Lu, H., Yang, G., Fei, N., Huo, Y., Lu, Z., Luo, P., Ding, M.: Vdt: General-purpose video diffusion transformers via mask modeling. arXiv preprint arXiv:2305.13311 (2023)

2023 arXiv
[18]

Peebles,W.,Xie,S.:Scalablediffusionmodelswithtransformers.In:Proceedingsof the IEEE/CVF international conference on computer vision. pp. 4195–4205 (2023)

2023
[19]

In: Computer Graphics Forum

Po, R., Yifan, W., Golyanik, V., Aberman, K., Barron, J.T., Bermano, A., Chan, E., Dekel, T., Holynski, A., Kanazawa, A., et al.: State of the art on diffusion models for visual computing. In: Computer Graphics Forum. vol. 43, p. e15063. Wiley Online Library (2024)

2024
[20]

European radiology 31(2), 1160–1174 (2021)

Rizk, J.: 4d flow mri applications in congenital heart disease. European radiology 31(2), 1160–1174 (2021)

2021
[21]

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
[22]

Inverse problems19(6), S165 (2003)

Strong, D., Chan, T.: Edge-preserving and scale-dependent properties of total vari- ation regularization. Inverse problems19(6), S165 (2003)

2003
[23]

Ad- vanced Engineering Informatics62, 102710 (2024)

Yang, Q., Tang, B., Deng, L., Zhu, P., Ming, Z.: Wtformer: Rul prediction method guided by trainable wavelet transform embedding and lagged penalty loss. Ad- vanced Engineering Informatics62, 102710 (2024)

2024
[24]

arXiv preprint arXiv:2401.14168 (2024)

Yang, Y., Xing, Z., Yu, L., Huang, C., Fu, H., Zhu, L.: Vivim: A video vision mamba for medical video segmentation. arXiv preprint arXiv:2401.14168 (2024)

2024 arXiv
[25]

Medical image analysis13(3), 483–493 (2009)

Zhao, F., Zhang, H., Wahle, A., Thomas, M.T., Stolpen, A.H., Scholz, T.D., Sonka, M.: Congenital aortic disease: 4d magnetic resonance segmentation and quantita- tive analysis. Medical image analysis13(3), 483–493 (2009)

2009

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

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

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsuper- vised learning model for deformable medical image registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9252–9260 (2018)

work page 2018

[2] [2]

IEEE transactions on medical imaging38(8), 1788–1800 (2019)

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE transactions on medical imaging38(8), 1788–1800 (2019)

2019

[3] [3]

IEEE Transactions on Emerging Topics in Computational Intel- ligence (2024)

Baniya, A.A., Lee, T.K., Eklund, P.W., Aryal, S.: A survey of deep learning video super-resolution. IEEE Transactions on Emerging Topics in Computational Intel- ligence (2024)

work page 2024

[4] [4]

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging37(11), 2514–2525 (2018)

2018

[5] [5]

Medical image analysis82, 102615 (2022)

Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: Transmorph: Transformer for unsupervised medical image registration. Medical image analysis82, 102615 (2022)

2022

[6] [6]

Behavior Research Methods, Instruments, & Computers25(2), 101–113 (1993)

Cohen, J.D., Noll, D.C., Schneider, W.: Functional magnetic resonance imaging: Overview and methods for psychological research. Behavior Research Methods, Instruments, & Computers25(2), 101–113 (1993)

work page 1993

[7] [7]

In: Proceedings of the AAAI Conference on Artificial Intelligence

Danier, D., Zhang, F., Bull, D.: Ldmvfi: Video frame interpolation with latent diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 1472–1480 (2024)

work page 2024

[8] [8]

Investigative radiology58(1), 60–75 (2023)

Eck, B.L., Yang, M., Elias, J.J., Winalski, C.S., Altahawi, F., Subhas, N., Li, X.: Quantitative mri for evaluation of musculoskeletal disease: cartilage and muscle composition, joint inflammation, and biomechanics in osteoarthritis. Investigative radiology58(1), 60–75 (2023)

work page 2023