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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".