Cardiac function evaluation in healthy volunteers and patients with implantable cardioverter-defibrillators using high-bandwidth spoiled gradient-echo cine
Bibliographic record
Abstract
BACKGROUND: inhomogeneity in conventional steady-state free precession (SSFP) cine sequences. Alternatively, high-bandwidth gradient-recalled echo (GRE) cine sequences can be used to minimize artifacts in the myocardium. In this study, we assessed the bias and interobserver variability in cardiac volumes and ejection fractions between GRE cines in acquired in the presence of ICDS and ground-truth SSFP cines (without ICDs present) in a population of healthy volunteers. Further, a small cohort of ICD patients was recruited and scanned to demonstrate clinical feasibility. METHODS: High-bandwidth GRE cine was performed in 11 healthy volunteers with taped ICDs mimicking clinical implants. After the ICD was removed, ground-truth SSFP cine was performed. Two observers separately assessed image quality metrics and contoured the cine images to return cardiac volumes and ejection fractions. Nine patients with an ICD were also scanned with the GRE cine protocol before contrast administration; data were contoured by two observers and analyzed for interobserver agreement. RESULTS: In the healthy volunteer dataset, no statistically significant differences were found when comparing volumes or ejection fractions between sequences (p > 0.05). Statistically significant differences were found when comparing right ventricular ejection fraction (RVEF) (p = 0.009) and right ventricular end-systolic volume (p = 0.029) between observers, with no other significant interobserver differences. The interobserver variability of patient left ventricular ejection fraction and RVEF data was 3-4%, with lower image quality metrics for patient scans than volunteer scans. CONCLUSION: GRE cine imaging in healthy volunteers with taped ICDs demonstrated good agreement with SSFP cine, but increased interobserver variability. In patients, reducing the breath-hold duration caused a decrease in image quality, with GRE cine imaging in patients with ICDs demonstrating poorer image quality and greater interobserver variability than in healthy volunteer studies. Future work is needed to improve GRE cine image quality in patients with ICDs to reduce interobserver variability and improve clinical confidence.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".