MAUDGAN: Motion Artifact Unsupervised Disentanglement Generative Adversarial Network of Multicenter MRI Data with Different Brain tumors
Bibliographic record
Abstract
Abstract Purpose This study proposed a novel retrospective motion reduction method named motion artifact unsupervised disentanglement generative adversarial network (MAUDGAN) that reduces the motion artifacts from brain images with tumors and metastases. The MAUDGAN was trained using a mutlimodal multicenter 3D T1-Gd and T2-fluid attenuated inversion recovery MRI images. Approach The motion artifact with different artifact levels were simulated in k -space for the 3D T1-Gd MRI images. The MAUDGAN consisted of two generators, two discriminators and two feature extractor networks constructed using the residual blocks. The generators map the images from content space to artifact space and vice-versa. On the other hand, the discriminators attempted to discriminate the content codes to learn the motion-free and motion-corrupted content spaces. Results We compared the MAUDGAN with the CycleGAN and Pix2pix-GAN. Qualitatively, the MAUDGAN could remove the motion with the highest level of soft-tissue contrasts without adding spatial and frequency distortions. Quantitatively, we reported six metrics including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). The MAUDGAN got the lowest NMSE and MS-GMSD. On average, the proposed MAUDGAN reconstructed motion-free images with the highest SSIM, PSNR, and VIF values and comparable MS-SSIM values. Conclusions The MAUDGAN can disentangle motion artifacts from the 3D T1-Gd dataset under a multimodal framework. The motion reduction will improve automatic and manual post-processing algorithms including auto-segmentations, registrations, and contouring for guided therapies such as radiotherapy and surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".