Accelerated reconstruction of highly undersampled 3D cardiac MRI image navigators
Bibliographic record
Abstract
Intraprocedural 3D real-time magnetic resonance imaging (MRI) provides a way for accurate and precise radiofrequency catheter targeting during ventricular tachycardia ablation. However, the limited data acquisition time needed to freeze cardiac motion results in highly undersampled k-space data that are challenging to reconstruct. In this work, we evaluated several deep learning (DL) based methods for real-time reconstruction of highly undersampled 3D real-time cardiac MRI. Algorithm reconstruction performance and speed were compared between classical algorithms and DL-based methods. Generative adversarial networks with attention layers in the generator were used to perform reconstructions in the image domain, which strived to balance reconstruction speed and image quality. In addition, variational networks were implemented by iterating data consistency in k-space and enforcing image smoothness via neural network-based regularization. In a preliminary study of heartbeat-resolved highly undersampled 3D cardiac MRI for 11 healthy volunteers, we observed that DL reconstruction methods provided good image quality with a significant increase in computational speed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".