High on sparsity: Interbin compensation of cardiac motion for improved assessment of left‐ventricular function using <scp>5D</scp> whole‐heart <scp>MRI</scp>
Bibliographic record
Abstract
Abstract Purpose Cardiac magnetic resonance is the gold standard for evaluating left‐ventricular ejection fraction (LVEF). Standard protocols, however, can be inefficient, facing challenges due to significant operator and patient involvement. Although the free‐running framework (FRF) addresses these challenges, the potential of the extensive data it collects remains underutilized. Therefore, we propose to leverage the large amount of data collected by incorporating interbin cardiac motion compensation into FRF (FRF‐MC) to improve both image quality and LVEF measurement accuracy, while reducing the sensitivity to user‐defined regularization parameters. Methods FRF‐MC consists of several steps: data acquisition, self‐gating signal extraction, deformation field estimations, and motion‐resolved reconstruction with interbin cardiac motion compensation. FRF‐MC was compared with the original 5D‐FRF method using LVEF and several image‐quality metrics. The cardiac regularization weight () was optimized for both methods by maximizing image quality without compromising LVEF measurement accuracy. Evaluations were performed in numerical simulations and in 9 healthy participants. In vivo images were assessed by blinded expert reviewers and compared with reference standard 2D‐cine images. Results Both in silico and in vivo results revealed that FRF‐MC outperformed FRF in terms of image quality and LVEF accuracy. FRF‐MC reduced temporal blurring, preserving detailed anatomy even at higher cardiac regularization weights, and led to more accurate LVEF measurements. Optimized produced accurate LVEF for both methods compared with the 2D‐cine reference (FRF‐MC: 0.59% [−7.2%, 6.0%], p = 0.47; FRF: 0.86% [−8.5%, 6.7%], p = 0.36), but FRF‐MC resulted in superior image quality (FRF‐MC: 2.89 ± 0.58, FRF: 2.11 ± 0.47; p < 10 −3 ). Conclusion Incorporating interbin cardiac motion compensation significantly improved image quality, supported higher cardiac regularization weights without compromising LVEF measurement accuracy, and reduced sensitivity to user‐defined regularization parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".