A <scp>3D stack‐of‐spirals</scp> approach for rapid hyperpolarized <scp><sup>129</sup>Xe</scp> ventilation mapping in pediatric cystic fibrosis lung disease
Bibliographic record
Abstract
Purpose To demonstrate the feasibility of a rapid 3D stack‐of‐spirals (3D‐SoS) imaging acquisition for hyperpolarized 129 Xe ventilation mapping in healthy pediatric participants and pediatric cystic fibrosis (CF) participants, in comparison to conventional Cartesian multislice (2D) gradient‐recalled echo (GRE) imaging. Methods The 2D‐GRE and 3D‐SoS acquisitions were performed in 13 pediatric participants (5 healthy, 8 CF) during separate breath‐holds. Images from both sequences were compared on the basis of ventilation defect percent (VDP) and other measures of image similarity. The nadir of transient oxygen saturation (SpO 2 ) decline due to xenon breath‐holding was measured with pulse oximetry, and expressed as a percent change relative to baseline. Results 129 Xe ventilation images were acquired in a breath‐hold of 1.2–1.8 s with the 3D‐SoS sequence, compared to 6.2–8.8 s for 2D‐GRE. Mean ± SD VDP measures for 2D‐GRE and 3D‐SoS sequences were 5.02 ± 1.06% and 5.28 ± 1.08% in healthy participants, and 18.05 ± 8.26% and 18.75 ± 6.74% in CF participants, respectively. Across all participants, the intraclass correlation coefficient of VDP measures for both sequences was 0.98 (95% confidence interval: 0.94–0.99). The percent change in SpO 2 was reduced to −2.1 ± 2.7% from −5.2 ± 3.5% with the shorter 3D‐SoS breath‐hold. Conclusion Hyperpolarized 129 Xe ventilation imaging with 3D‐SoS yielded images approximately five times faster than conventional 2D‐GRE, reducing SpO 2 desaturation and improving tolerability of the xenon administration. Analysis of VDP and other measures of image similarity demonstrate excellent agreement between images obtained with both sequences. 3D‐SoS holds significant potential for reducing the acquisition time of hyperpolarized 129 Xe MRI, and/or increasing spatial resolution while adhering to clinical breath‐hold constraints.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".