Deep Learning-Based Breath-Hold and Free-Breathing Cine MRI for Comprehensive Cardiac Evaluation
Bibliographic record
Abstract
Objective: To evaluate and compare scan times, measurement accuracy, and image quality (IQ) of free-breathing (FB) and breath-hold (BH) deep learning (DL) cine MRI sequences versus standard cine MRI, with a specific focus on patients with arrhythmia and dyspnea.Materials and Methods: Seventy participants were prospectively enrolled, including 24 with arrhythmia, 17 with dyspnea, and 29 with normal sinus rhythm and eupnea (mean age, 49 ± 17 years).Each patient underwent three cine MRI acquisitions (standard cine, BHDL, and FBDL) on a 3T scanner.Quantitative assessments of biventricular function, left ventricular mass, and myocardial strain were independently performed by three radiologists, blinded to image acquisition techniques.IQ was evaluated by the same readers using both a five-point Likert scale and objective metrics.Results: Both BHDL and FBDL significantly reduced total examination times compared to standard cine (BHDL: 58 ± 5 s; FBDL: 88 ± 12 s; standard cine: 208 ± 12 s; adjusted P < 0.001).Quantitative measurements from BHDL and FBDL showed no statistically significant differences compared to standard cine and showed strong correlations (correlation coefficients > 0.85) with standard cine.BHDL consistently demonstrated narrower 95% limits of agreement (LOA) than FBDL across all parameters.For BHDL, the 95% LOA for left and right ventricular ejection fractions were -3.5% to 3.9% and -3.4% to 4.0%, respectively; for FBDL, they were -4.6% to 5.8% and -7.8% to 9.3%, respectively.In patients with arrhythmia, BHDL achieved significantly higher IQ Likert scores (4.44 ± 0.56) than both standard cine (4.00 ± 0.99; adjusted P = 0.043) and FBDL (3.94 ± 0.56; adjusted P = 0.030).In patients with dyspnea, FBDL received the highest IQ scores (4.24 ± 0.47), outperforming standard cine (3.41 ± 0.97; adjusted P = 0.028) and BHDL (3.68 ± 0.56; adjusted P = 0.028).Conclusion: Both FBDL and BHDL significantly reduced scan times compared to standard cine without compromising quantitative measurement accuracy.BHDL offered superior measurement accuracy and shorter scan time than FBDL.Furthermore, BHDL demonstrated robust suitability for patients with arrhythmia by minimizing arrhythmia-related artifacts, whereas FBDL was more effective in patients with dyspnea by mitigating respiratory motion artifacts.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".