MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction

Andrew Zhang; Hao Wang; Jinman Kim; Michael Fulham; Shuchang Ye

REVIEW 5 major objections 6 minor 19 references

MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read MAISY claims CT motion correction improves sharply when an attention-guided GAN is steered by SAM anatomical masks and a variance-selective SSIM loss that focuses on high-variance patches.

desk verdict Solid incremental CT motion-correction paper with honest ablations, but the abstract overstates gains and the phantom-only evaluation needs real-data validation before the clinical claims can be taken at face value. read the letter →

arxiv 2505.04105 v3 pith:GPYZ6ZLE submitted 2025-05-07 eess.IV cs.CV

Andrew Zhang , Hao Wang , Shuchang Ye , Michael Fulham , Jinman Kim This is my paper · ORCID

classification eess.IVcs.CV

keywords motioncorrectionCTGANSegmentAnythingModelSSIMlossvariance-selectiveXCATphantomartifacts

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

MAISY is a generative method for correcting patient motion artifacts in CT images. It claims that motion correction improves when the network is explicitly told where artifacts concentrate: a Segment Anything Model (SAM) mask marks anatomical boundaries, and a Variance-Selective SSIM (VS-SSIM) loss focuses training on high-variance patches, the regions most affected by motion. On simulated chest and head CT XCAT phantom data, the paper reports the highest PSNR, SSIM, and Dice among all compared methods; against the second-best method, AGGAN, MAISY gains 3.20% PSNR on the chest dataset and 13.91% PSNR on the head-and-jaw dataset. The significance is that motion artifacts, which blur organs and obscure pathology, can be removed without globally smoothing away diagnostically useful local detail.

What carries the argument

The argument is carried by two complementary modules. The first is SAM-based masking: the Segment Anything Model produces a pre-binarized mask feature map that semantically marks anatomical boundaries, acting as a pre-attention guide for the AGGAN's attention mask. The second is the VS-SSIM loss, a patch-level structural similarity loss that selects the top-variance patches $\Omega$, computes SSIM on each patch, and averages them. Together these modules concentrate the generator's capacity on the anatomical regions where motion artifacts are strongest, while the adversarial and cycle losses preserve global structure.

What would settle it

Collect real clinical CT exams with motion artifacts and expert-validated artifact-free reconstructions, run MAISY and the strongest baseline AGGAN on them, and compare PSNR, SSIM, Dice, and radiologist readings; if MAISY's margins shrink to near zero or reverse, the paper's central claim fails.

Watch

Extended reading notes

Core claim

MAISY's central claim is that CT motion correction benefits from combining anatomical localization with a variance-weighted structural loss. The method computes a mask feature map $F = D(E(y_{input}), P)$ from SAM using point prompts, concatenates it with the motion-corrupted image, and feeds the result into an Attention Guided GAN (AGGAN) whose attention mask $M$ highlights regions needing correction. The VS-SSIM loss ranks local patches by pixel variance and averages SSIM over the top-variance patches $\Omega$, giving $L_{VS\text{-}SSIM} = 1 - \frac{1}{|\Omega|}\sum_{i\in\Omega}\mathrm{SSIM}(y_{gt,i}, y_{gen,i})$; the total loss combines this with adversarial and cycle-consistency terms. On chest CT, MAISY reports PSNR 43.573, SSIM 0.989, and Dice 0.991 versus AGGAN's 42.211, 0.982, and 0.983, and on the head-and-jaw dataset PSNR 43.372 versus 38.075, with the gains attributed to focusing correction along anatomical boundaries instead of applying global smoothing.

Load-bearing premise

The load-bearing premise is that motion artifacts created by computer simulation on XCAT phantom CT images behave like motion artifacts in real patient CT scans, so phantom-measured improvements will transfer to clinical use.

Editorial extensions

If this is right

MAISY's modules are portable: the paper's modularity study shows that adding SAM masking and VS-SSIM to cGAN, Pix2Pix, and Cycle GAN raises their PSNR, SSIM, and Dice.
The method transfers to a new anatomy with little retraining: after 10 epochs of fine-tuning on 20 head-and-jaw scans, MAISY stays accurate while the comparison methods drop in performance when fine-tuned on a different anatomy.
Dice scores near 0.99 indicate that corrected images preserve organ boundaries, so motion correction does not have to sacrifice structural fidelity.
Concentrating the loss on high-variance patches should protect small, diagnostically relevant details from being smoothed away.
If the XCAT simulation faithfully models clinical motion, the reported margins should translate directly into better artifact removal on real chest and head CT acquisitions.

Reading between the lines

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

Beyond the paper: the headline gains of 40% PSNR, 10% SSIM, and 16% Dice in the abstract are computed against the weak cGAN baseline; compared with the closest competitor, AGGAN, the chest-dataset gains are 3.20% PSNR, 0.71% SSIM, and 0.81% Dice.
Beyond the paper: all results come from simulated XCAT phantom scans with synthetic rigid and non-rigid motion, so unmeasured clinical factors such as breathing, cardiac motion, and peristalsis could narrow the margins in real patient data.
Beyond the paper: the SAM prompts are manual point prompts, so a clinical deployment would need automatic prompt selection; the paper does not specify how that would work.
Beyond the paper: the variance-selective loss idea is not tied to the GAN backbone and could be tested in diffusion-based correction models to see whether the loss or the architecture drives the improvement.

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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

Colleague,

Two things to know about arXiv:2505.04105. First, it is a legitimate incremental contribution: combining SAM-generated boundary masks with an attention GAN and a new variance-selective SSIM loss for CT motion correction. The ablations and modularity study are the best part—they show each component adds value across three baseline architectures, and the method fine-tunes to a new anatomical region (head/jaw) with only 20 images, which is a real plus. Second, the abstract's 40%/10%/16% gains are against the weakest baseline (cGAN), not against the state of the art (AGGAN); the actual margins over AGGAN are 3.2% PSNR, 0.7% SSIM, 0.8% Dice on chest. That is a meaningful difference but not the story the abstract tells.

The method itself is sensible. The VS-SSIM loss that selects the top-variance patches is a reasonable patch-level extension of SSIM, and using SAM as a prior to focus correction inside anatomical boundaries is well motivated. The paper is clearly written and the setup is reproducible in principle.

The soft spots are real and mostly cluster around the evaluation. All training and test images are XCAT phantoms with computer-simulated rigid/non-rigid motion. No real patient CT with motion artifacts is used anywhere. The limitation section only says other modalities were not tested—it omits the more important caveat that no real data was used. For a paper whose title says 'medical image motion correction' and whose abstract suggests clinical relevance, that is a serious gap. The reported numbers may not survive contact with real patient motion, which includes breathing drift, twitches, and metal/beam-hardening interactions that the simulator may not capture.

Also missing: error bars, significance tests, and several hyperparameters (loss weights, the number of top patches, SAM prompt placement). These are minor in comparison but should be addressed.

The internal comparison is fair—held-out test split, same simulated motion pipeline for all methods—so the relative ranking is credible within the simulation. The paper is best read as a proof-of-concept for combining foundation-model masks with attention GANs and adaptive losses. It is for researchers working on deep learning CT motion correction, not for radiologists seeking a clinically validated tool.

Yes, this deserves peer review. It is a solid engineering paper with a clear experimental design. A good reviewer should ask for real-data validation, error bars, and an honest abstract before acceptance; those are reasonable major revisions.

Referee Report

5 major / 6 minor

Summary. The manuscript proposes MAISY, a GAN-based framework for correcting patient motion artifacts in CT images. The method combines a pre-trained Segment Anything Model (SAM) to produce anatomical mask features that are concatenated with the input to an Attention-Guided GAN (AGGAN), and a new Variance-Selective SSIM (VS-SSIM) loss that averages SSIM over the highest-variance patches. Experiments on simulated XCAT chest and head/jaw CT datasets are compared with U-Net, cGAN, Pix2Pix, Cycle GAN, and AGGAN, and the authors report improvements in PSNR, SSIM, and Dice, along with ablation and modularity studies. The abstract claims a 40% PSNR increase, 10% SSIM increase, and 16% Dice increase, which turn out to be relative to cGAN, not the strongest baseline.

Significance. If the reported results are reproducible, the paper makes a useful engineering contribution: adding SAM-derived structural guidance and a variance-weighted patch SSIM loss to an existing GAN architecture yields consistent improvements on the two simulated datasets and also improves the performance of other generators when grafted onto them. The modularity study is a strength, as it demonstrates that the proposed modules are transferable. However, the clinical significance of the work is not established because all experiments are based on simulated phantom data, and the headline improvement figures in the abstract are measured against a weak baseline. The evaluation also lacks error bars or statistical tests, making it difficult to assess whether the claimed margins are meaningful.

major comments (5)

[Abstract and Table 1] The abstract states that PSNR increases by 40%, SSIM by 10%, and Dice by 16%, but these figures are relative to the cGAN baseline (PSNR 31.210, SSIM 0.901, Dice 0.855 on chest). In contrast, the improvement over the strongest baseline AGGAN is only 3.20% PSNR, 0.71% SSIM, and 0.81% Dice on the chest dataset. Since the paper's central claim is that MAISY outperforms state-of-the-art counterparts, the comparison should be against the best-performing baseline, not a weaker one. The abstract overstates the gains and should be revised to reference the correct baseline.
[Section 3.1 and Section 5] All experiments use simulated XCAT phantom data with computer-simulated rigid and non-rigid motion. No real patient CT volumes with motion artifacts are used, and Section 5 only notes that other modalities were not tested; it does not acknowledge the absence of real acquisition data. If real patient motion includes patterns absent from the simulator (e.g., discontinuous breathing, organ drift, or interaction with metal and beam hardening), the reported gains over AGGAN may not transfer to clinical scans. The authors should either include a real-data validation or substantially temper the claims of clinical applicability.
[Section 2.2, Eq. (5)] The definition of VS-SSIM is ambiguous and irreproducible as written. The equation contains an unexplained prefactor 3, while the text says the SSIM values are 'averaged' over Ω patches, which would correspond to 1/|Ω|. If the factor 3 is a typo, it should be corrected; if intentional, its purpose and effect on the loss bounds must be explained. Additionally, the number of top-variance patches Ω, the patch size, and the loss balancing weights λ1 and λ2 are never specified anywhere in the paper. Without these hyperparameters, the method cannot be independently reproduced.
[Section 2.2 and Eq. (1)] The training data setup is unclear. Equation (1) and the VS-SSIM loss use the ground truth y_gt, which implies paired training data. However, the text also says that AGGAN compares the generated output against 'an unpaired motion-free target image' and uses a cycle loss. It is not clear whether the authors used paired, unpaired, or semi-supervised training. This distinction is essential for interpreting the loss function and for understanding why a cycle consistency loss is included when ground truth images appear to be available. The authors should clarify the training paradigm.
[Tables 1, 3, and 4] No error bars, confidence intervals, or significance tests are reported for any of the quantitative results. The head-and-jaw test set consists of only 20 volumes, so the differences between methods (e.g., Dice 0.998 vs. 0.999) may not be statistically meaningful. The authors should report standard deviations across repeated runs or bootstrap confidence intervals to support the claim that the improvements are consistent and not simply due to random variation.

minor comments (6)

[Introduction] There are typographical errors: 'bowl' should be 'bowel' (involuntary motion examples), and 'Patient motions encompasses' should be 'Patient motion encompasses'. Also, 'sematic' in Section 2.1 should be 'semantic'.
[Section 3.2] The Dice score computation is incompletely described: the paper states that a threshold is applied to create a binary mask, but the threshold value is not provided. Please specify the thresholding procedure.
[Section 3.1] The abbreviation 'FBCT' is not defined; the text says 'step-and-shoot flat-bed CT (FBCT)' but does not explain what FBCT stands for in this context or provide a description of the geometry.
[Section 2.2, Eq. (4)] The symbols R_gen and M_gen (content mask and attention mask) are not defined in the text. They appear to be borrowed from the original AGGAN, but the adaptation should be clarified so the reader can follow the architecture.
[Section 2.1] The mask feature map is described as 'pre-binarized', but the binarization threshold is not given. Also, the paper does not state whether the SAM encoder is frozen or fine-tuned during training, which affects the interpretation of the 'dynamic learning' claim.
[General] The table numbering skips Table 2: the manuscript jumps from Table 1 in Section 4.1 to Table 3 in Section 4.2. Please renumber or add the missing table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the method is an empirical combination of external modules evaluated on a held-out test split, and no claimed result reduces to its own inputs.

full rationale

This is an empirical systems paper rather than a derivation chain, so the circularity patterns do not apply in a load-bearing way. The proposed method concatenates a SAM-derived mask feature map with the input CT image and feeds it into an AGGAN baseline augmented with a VS-SSIM loss (Eqs. 2-7). The VS-SSIM loss is defined independently of the evaluation metrics PSNR, SSIM, and Dice: it ranks local patches by pixel variance and applies SSIM only to the top-variance patches, and it is not fitted to produce the reported scores. The same loss and masking module are applied across two datasets and in the modularity study, which further indicates that the results are not tuned by construction. The AGGAN backbone is cited to Tang et al. [14] and the SAM backbone to Kirillov et al. [13], both external to the authors; there is no self-citation chain that forces the method's choice or its evaluation outcome. The abstract's '40% PSNR, 10% SSIM, 16% Dice' figures are improvements relative to the cGAN baseline rather than the strongest competitor AGGAN, and Table 1 shows much smaller gains over AGGAN; this is an overstatement in result reporting, not a circularity. Similarly, the fact that all training and test data are simulated XCAT phantoms with computer-generated motion is a limitation on clinical generalizability, not a circular dependence of the claims on their own assumptions. No equation or fitted parameter is equivalent to the claimed conclusion by definition, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper contributes an empirical network design plus a loss variant; there are no physics or math derivations. The central performance claim depends on unstated loss weights, patch selection, and prompt generation, and on the representativeness of simulated XCAT motion for real clinical motion.

free parameters (4)

Loss balancing weights lambda_1 and lambda_2
Equation (7) combines VS-SSIM, GAN, and cycle losses with weights lambda_1 and lambda_2, but their values are never reported; the final result depends on these choices.
Number of top-variance patches Omega in VS-SSIM
VS-SSIM (Eq. 5) averages SSIM over the top-variance patches Omega; the criterion for how many patches are in Omega and the patch size are not specified, and the choice affects the loss and results.
SSIM patch size for VS-SSIM
SSIM is computed on localized patches; patch size and stride are not given, yet they determine the variance ranking and the preserved detail.
SAM point prompts P
SAM's mask feature map F is extracted using 'manual user prompts P' (Section 2.1); the number, position, and generation procedure for prompts are not described, which affects the mask and thus the correction.

assumptions (4)

domain assumption XCAT phantom with simulated rigid and non-rigid motion accurately represents real patient motion artifacts in CT.
Section 3.1 uses motion simulated with step-and-shoot FBCT geometry and spline interpolation; if the simulation differs from real motion, the comparison results are not clinically valid.
domain assumption SAM, pretrained on natural images, can segment anatomical regions in CT without fine-tuning.
Section 2.1 uses SAM to extract mask feature maps from CT input; this transfer is assumed valid.
domain assumption PSNR, SSIM, and Dice are valid metrics for motion correction quality.
Section 3.2 adopts these as established metrics; Dice is computed by thresholding a segmentation result, which may not reflect clinically relevant organ segmentation.
domain assumption The AGGAN architecture from Tang et al. is a suitable base for CT image-to-image translation.
Section 2.2 reuses AGGAN; its design choices are inherited without re-validation on CT.

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Cite this review

Pith. "Pith review of MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction." pith.science (2026). https://pith.science/paper/GPYZ6ZLE

@misc{pith2026250504105,
  author       = {Pith},
  title        = {Pith review of: MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GPYZ6ZLE}},
  note         = {Machine review of arXiv:2505.04105}
}

read the original abstract

Patient motion during medical image acquisition causes blurring, ghosting, and distorts organs, which makes image interpretation challenging. Current state-of-the-art algorithms using Generative Adversarial Network (GAN)-based methods with their ability to learn the mappings between corrupted images and their ground truth via Structural Similarity Index Measure (SSIM) loss effectively generate motion-free images. However, we identified the following limitations: (i) they mainly focus on global structural characteristics and therefore overlook localized features that often carry critical pathological information, and (ii) the SSIM loss function struggles to handle images with varying pixel intensities, luminance factors, and variance. In this study, we propose Motion-Aware Image SYnthesis (MAISY) which initially characterize motion and then uses it for correction by: (a) leveraging the foundation model Segment Anything Model (SAM), to dynamically learn spatial patterns along anatomical boundaries where motion artifacts are most pronounced and, (b) introducing the Variance-Selective SSIM (VS-SSIM) loss which adaptively emphasizes spatial regions with high pixel variance to preserve essential anatomical details during artifact correction. Experiments on chest and head CT datasets demonstrate that our model outperformed the state-of-the-art counterparts, with Peak Signal-to-Noise Ratio (PSNR) increasing by 40%, SSIM by 10%, and Dice by 16%.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 11 canonical work pages

[1]

Here, the image encoder extracts the image embeddings from the image which is then passed through a mask decoder

Overall framework of MAISY 2.1 SAM Based Image Synthesis The first approach to MAISY is the use of SAM to segment the anatomical regions. Here, the image encoder extracts the image embeddings from the image which is then passed through a mask decoder. We provided point prompts within the CT image where the image embedding is passed into a mask decoder whe...

work page 2000
[2]

Deep learning-based rigid motion correction for magnetic resonance imaging: A survey,

Y. Chang, Z. Li, G. Saju, H. Mao, and T. Liu, "Deep learning-based rigid motion correction for magnetic resonance imaging: A survey," Meta-Radiology, vol. 1, no. 1, p. 100001, 2023/06/01/ 2023, doi: https://doi.org/10.1016/j.metrad.2023.100001

arXiv 2023
[3]

MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach,

K. Pawar, Z. Chen, N. J. Shah, and G. F. Egan, "MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach," arXiv preprint arXiv:1807.10831,

arXiv
[4]

How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis,

M. J. M. Chuquicusma, S. Hussein, J. Burt, and U. Bagci, "How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis," in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 4-7 April 2018 2018, pp. 240-244, doi: 10.1109/ISBI.2018.8363564

arXiv 2018
[5]

Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks,

K. Usui et al., "Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks," Scientific Reports, vol. 13, no. 1, p. 8526, 2023/05/26 2023, doi: 10.1038/s41598-023-35794-1

work page doi:10.1038/s41598-023-35794-1 2023
[6]

Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network,

M. Usman, S. Latif, M. Asim, B.-D. Lee, and J. Qadir, "Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network," Scientific Reports, vol. 10, no. 1, p. 4786, 2020/03/16 2020, doi: 10.1038/s41598-020-61705-9

work page doi:10.1038/s41598-020-61705-9 2020
[7]

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic <sup>18</sup>F-FDG PET Brain Studies,

L. K. Shiyam Sundar et al., "Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic <sup>18</sup>F-FDG PET Brain Studies," Journal of Nuclear Medicine, vol. 62, no. 6, pp. 871-879, 2021, doi: 10.2967/jnumed.120.248856

work page doi:10.2967/jnumed.120.248856 2021
[8]

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN,

M. Safari, X. Yang, C.-W. Chang, R. L. J. Qiu, A. Fatemi, and L. Archambault, "Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN," Physics in Medicine & Biology, vol. 69, no. 11, p. 115057, 2024/05/30 2024, doi: 10.1088/1361-6560/ad4845

work page doi:10.1088/1361-6560/ad4845 2024

Show all 19 references

[9]

Unsupervised Adversarial Correction of Rigid MR Motion Artifacts,

K. Armanious, A. Tanwar, S. Abdulatif, T. Küstner, S. Gatidis, and B. Yang, "Unsupervised Adversarial Correction of Rigid MR Motion Artifacts," in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 3-7 April 2020 2020, pp. 1494-1498, doi: 10.1109/ISBI45749.20...

2020
[10]

Learning MRI artefact removal with unpaired data,

[S. Liu, K.-H. Thung, L. Qu, W. Lin, D. Shen, and P.-T. Yap, "Learning MRI artefact removal with unpaired data," Nature Machine Intelligence, vol. 3, no. 1, pp. 60-67, 2021/01/01 2021, doi: 10.1038/s42256-020-00270-2

2021 doi
[11]

Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation,

G. Oh, J. E. Lee, and J. C. Ye, "Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation," IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 3125-3139, 2021, doi: 10.1109/TMI.2021.3089708

2021
[12]

Understanding ssim,

J. Nilsson and T. Akenine-Möller, "Understanding ssim," arXiv preprint arXiv:2006.13846,

2006 arXiv
[13]

Segment anything,

A. Kirillov et al., "Segment anything," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4015-4026

2023
[14]

Attentiongan: Unpaired image-to-image translation using attention-guided generative adversarial networks,

H. Tang, H. Liu, D. Xu, P. H. Torr, and N. Sebe, "Attentiongan: Unpaired image-to-image translation using attention-guided generative adversarial networks," IEEE transactions on neural networks and learning systems, vol. 34, no. 4, pp. 1972-1987,

1972
[15]

Rigid and non-rigid motion artifact reduction in X-ray CT using attention module,

Y. Ko, S. Moon, J. Baek, and H. Shim, "Rigid and non-rigid motion artifact reduction in X-ray CT using attention module," Medical Image Analysis, vol. 67, p. 101883, 2021/01/01/ 2021, doi: https://doi.org/10.1016/j.media.2020.101883. 10 Zhang et al

2021
[16]

Image-to-image translation with conditional adversarial networks,

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, "Image-to-image translation with conditional adversarial networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1125-1134

2017
[17]

High-resolution image synthesis and semantic manipulation with conditional gans,

T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, "High-resolution image synthesis and semantic manipulation with conditional gans," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8798-8807

2018
[18]

Semantic image synthesis with spatially-adaptive normalization,

T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, "Semantic image synthesis with spatially-adaptive normalization," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 2337-2346

2019
[19]

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping,

Q. Zhang et al., "Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping," Artificial Intelligence in Medicine, vol. 110, p. 101955, 2020/11/01/ 2020, doi: https://doi.org/10.1016/j.artmed.2020.101955

2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Here, the image encoder extracts the image embeddings from the image which is then passed through a mask decoder

Overall framework of MAISY 2.1 SAM Based Image Synthesis The first approach to MAISY is the use of SAM to segment the anatomical regions. Here, the image encoder extracts the image embeddings from the image which is then passed through a mask decoder. We provided point prompts within the CT image where the image embedding is passed into a mask decoder whe...

work page 2000

[2] [2]

Deep learning-based rigid motion correction for magnetic resonance imaging: A survey,

Y. Chang, Z. Li, G. Saju, H. Mao, and T. Liu, "Deep learning-based rigid motion correction for magnetic resonance imaging: A survey," Meta-Radiology, vol. 1, no. 1, p. 100001, 2023/06/01/ 2023, doi: https://doi.org/10.1016/j.metrad.2023.100001

arXiv 2023

[3] [3]

MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach,

K. Pawar, Z. Chen, N. J. Shah, and G. F. Egan, "MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach," arXiv preprint arXiv:1807.10831,

arXiv

[4] [4]

How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis,

M. J. M. Chuquicusma, S. Hussein, J. Burt, and U. Bagci, "How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis," in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 4-7 April 2018 2018, pp. 240-244, doi: 10.1109/ISBI.2018.8363564

arXiv 2018

[5] [5]

Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks,

K. Usui et al., "Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks," Scientific Reports, vol. 13, no. 1, p. 8526, 2023/05/26 2023, doi: 10.1038/s41598-023-35794-1

work page doi:10.1038/s41598-023-35794-1 2023

[6] [6]

Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network,

M. Usman, S. Latif, M. Asim, B.-D. Lee, and J. Qadir, "Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network," Scientific Reports, vol. 10, no. 1, p. 4786, 2020/03/16 2020, doi: 10.1038/s41598-020-61705-9

work page doi:10.1038/s41598-020-61705-9 2020

[7] [7]

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic <sup>18</sup>F-FDG PET Brain Studies,

L. K. Shiyam Sundar et al., "Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic <sup>18</sup>F-FDG PET Brain Studies," Journal of Nuclear Medicine, vol. 62, no. 6, pp. 871-879, 2021, doi: 10.2967/jnumed.120.248856

work page doi:10.2967/jnumed.120.248856 2021

[8] [8]

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN,

M. Safari, X. Yang, C.-W. Chang, R. L. J. Qiu, A. Fatemi, and L. Archambault, "Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN," Physics in Medicine & Biology, vol. 69, no. 11, p. 115057, 2024/05/30 2024, doi: 10.1088/1361-6560/ad4845

work page doi:10.1088/1361-6560/ad4845 2024