UMIC: Super-Resolution of Cine Cardiac MRI Using U-Shaped Network With Multi-Level Information Compensation
Bibliographic record
Abstract
In cine cardiac magnetic resonance imaging (CMRI), deep learning-based super-resolution (SR) reconstruction algorithms often suffer from feature information loss during feature extraction and lack effective mechanisms for feature compensation. These problems can lead to the lack of texture and edge details in the reconstructed image, making it difficult to obtain a clear cardiac image, which will increase the rate of misjudgment of cardiac disease by experts. To address these issues, we propose a U-shaped network with multi-level information compensation (UMIC). Specifically, the network first performs multi-level feature extraction on low-resolution (LR) inputs and reduces channel dimensionality via a downward channel branch. The compressed features are then fused through a bottom module to capture inter-channel dependencies. Finally, the relevant features are recovered and enhanced through an upward channel branch. Additionally, we introduce a multi-level information compensation module to mitigate detail loss incurred during channel compression and to assist in recovering difficult-to-restore LR image details in the reconstruction phase. Extensive experiments show that UMIC achieves better CMRI SR reconstruction performance compared to some state-of-the-art SR methods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".