Comparison of 3D UTE free‐breathing lung MRI with hyperpolarized <sup>129</sup>Xe MRI in pediatric cystic fibrosis
Bibliographic record
Abstract
Abstract Purpose To compare phase‐resolved functional lung (PREFUL) regional ventilation derived from a free breathing 3D UTE radial MRI acquisition to hyperpolarized 129Xe‐MRI (Xe‐MRI), conventional 2D multi‐slice PREFUL MRI, and pulmonary function tests in pediatric cystic fibrosis (CF) lung disease. Methods Free‐breathing 3D UTE and 2D multi‐slice 1H MRI as well as Xe‐MRI were acquired in 12 stable pediatric CF patients. Using PREFUL, regional ventilation (RVent) maps were calculated from the free‐breathing data. Ventilation defect percentage (VDP) was determined from 3D and 2D RVent maps (2D VDPRVent and 3D VDPRVent, respectively) and Xe‐MRI ventilation (VDPXe). VDP was calculated for the whole lung and for eight regions based on left/right, anterior/posterior, and superior/inferior divisions of the lung. Global and regional VDP was compared between the three methods using Bland–Altman analysis, linear mixed model‐based correlation, and one‐way analysis of variance and multiple comparisons tests. Results Global 3D VDPRVent, VDPXe, and 2D VDPRVent were all strongly correlated (all R2 > 0.62, p < 0.0001) and showed minimal, non‐significant bias (all <2%, p > 0.05). Three dimensional and 2D VDPRVent significantly correlated to VDPXe in most of the separate lung regions (R2 = 0.18–0.74, p < 0.04), but showed lower inter‐agreement. The superior/anterior lung regions showed the least agreement between all three methods (all p > 0.12). Conclusion Absolute VDP assessed by 3D UTE PREFUL MRI showed good global agreement with Xe‐MRI and 2D multi‐slice PREFUL MRI in pediatric CF lung disease. Therefore, 3D UTE PREFUL MRI offers a sensitive and potentially more accessible alternative to Xe‐MRI for regional volumetric evaluation of ventilation.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".