Rapid single‐breath hyperpolarized noble gas MRI‐based biomarkers of airspace enlargement
Bibliographic record
Abstract
Background Multi‐b diffusion‐weighted hyperpolarized‐gas MRI measures pulmonary airspace‐enlargement using apparent diffusion coefficients (ADCs) and mean‐linear‐intercepts ( L m ). Purpose To develop single‐breath 3D multi‐b diffusion‐weighted 3 He and 129 Xe MRI using k ‐space undersampling. Rapid, cost‐efficient, single‐breath acquisitions may facilitate clinical translation. Study Type Prospective. Subjects We evaluated 12 participants, including nine subjects (mean age = 69 ± 9) who were included in the retrospective experiment and three chronic pulmonary obstruction disease (COPD) patients (mean age = 81 ± 6) who participated in the prospective study. Field Strength A whole‐body 3 T 2D/3D fast gradient recall echo (FGRE) sequence. Assessment Hyperpolarized 3 He/ 129 Xe MRI, spirometry, plethysmography computed tomography (CT). We evaluated 129 Xe ADC/morphometry estimates by retrospectively undersampling previously acquired fully sampled multibreath, multi‐b diffusion‐weighted data. Next, we prospectively evaluated the feasibility of accelerated (AF = 7) 3 He MRI static‐ventilation/T 2 * (extra short‐TE, b = 0 image) and ADC/morphometry (five b ‐values) maps using a single gas‐dose and 16‐second breath‐hold. To conservatively evaluate cost‐improvement, we compared total costs of single vs. multiple 129 Xe doses. Statistical Tests Multivariate analysis of variance, independent t ‐tests and voxel‐by‐voxel basis difference test. Results For the retrospectively undersampled 129 Xe data, a nonsignificant mean difference for ADC/ L m of 14%/12%, 12%/8%, and 11%/9% was observed (all, P > 0.4) between the fully sampled and accelerated data for the never‐smoker, COPD, and alpha‐1 antitrypsin deficiency (AATD) groups, respectively. The control never‐smoker group had significantly lower ADC ( P < 0.001) and L m ( P < 0.001) than the COPD/AATD group for both fully sampled and accelerated data. For the prospectively acquired 3 He MRI data, static‐ventilation, T 2 *, ADC, and morphometry maps were acquired using a single 16‐second breath‐hold scan and single gas dose. Accelerated imaging resulted in cost savings of ~$US 1000/patient, a conservative estimate based on 129 Xe MRI dose savings (single vs. five doses). Data Conclusion This is a proof‐of‐concept demonstration of accelerated (7×) morphometry that shows that less cost‐ and time‐efficient multibreath methods that lead to variability and patient fatigue may be avoided in the future. Level of Evidence: 2 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.
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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 | 0.001 | 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 | 0.000 |
| 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 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".