Right Ventricular Imaging in 25 Seconds
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to prospectively evaluate a 2-dimensional real-time CINE TrueFISP magnetic resonance sequence using sparse data sampling with iterative reconstruction (SSIR) for right ventricular (RV) volumetry in comparison to the criterion standard (CS) acquired at 3 T. MATERIALS AND METHODS: Ten healthy controls and 20 consecutive patients scheduled for cardiac magnetic resonance imaging on a 3-T system (Magnetom Skyra; Siemens Healthcare Sector, Germany) underwent undersampled SSIR sequences with a single breath-hold (BH) as well as with shallow free breathing (NBH) and a fully sampled multi-BH sequence as CS. Right ventricular volumetry was performed with dedicated cardiac magnetic resonance software (cvi42; Circle Cardiovascular Imaging Inc, Calgary, Alberta, Canada). Agreement of SSIR with and without BH and CS for RV functional parameters (end-systolic volume [RVESV], end-diastolic volume [RVEDV], stroke volume [RVSV], and ejection fraction [RVEF]) were assessed with Bland-Altman analysis and paired t test. RESULTS: Analysis of the 30 individuals (19 male; 48 ± 14 years) revealed no significant differences when comparing CS and BH measurements for RVEDV (153.7 vs 153.6 mL, P = 0.96), RVESV (71.6 vs 72.1 mL, P = 0.78), RVSV (82.0 vs 81.6 mL, P = 0.65), and RVEF (54.9% vs 54.2%, P = 0.19). Similar results were shown when comparing CS and NBH measurements for RVEDV (153.7 vs 152.2 mL, P = 0.34), RVESV (71.6 vs 72.8 mL, P = 0.30), RVSV (82.0 vs 81.0 mL, P = 0.46), and RVEF (54.9 vs 54.4, P = 0.48). Time taken for acquisition was 350 seconds for the CS, 34 seconds for BH, and 25 seconds for NBH measurements. Additional time required for iterative reconstruction was 2 minutes and 30 seconds for the sparse sampled data sets. CONCLUSIONS: Our results demonstrate that accurate RV volumetry with SSIR data at 3 T is feasible in clinical routine within 25 seconds even without BH, which is of particular importance in patients with dyspnea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".