Segmentation-Guided Diffusion for Free-Breathing Cardiac Magnetic Resonance Image Restoration
Bibliographic record
Abstract
Cardiac magnetic resonance imaging (CMR) is considered the gold standard for assessing cardiac function. However, acquiring high-quality images typically requires patients to hold their breath during scanning. Free-breathing (FB) CMR serves as an alternative for patients who cannot hold their breath; however, it often introduces motion artifacts, degrading image quality and potentially affecting diagnostic accuracy. Although deep generative models have shown promise in correcting motion artifacts, ensuring confidence in the fidelity of reconstructed artifact-free images remains a significant concern. This study quantifies the impact of segmentation masks as guidance in diffusion models to enhance anatomical structure preservation during image-conditioned generation. To that end, a standard diffusion probabilistic model (DDPM) and a segmentation-guided DDPM are trained and evaluated on a public CMR dataset and further applied to restore FB CMR using local hospital data. Quantitative evaluations using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) over the left and right ventricular regions demonstrate that the segmentation-guided approach produces higher-quality CMR and more accurately preserves anatomical structures compared to the standard DDPM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".