Accelerated <sup>129</sup>Xe MRI morphometry of terminal airspace enlargement: Feasibility in volunteers and those with alpha‐1 antitrypsin deficiency
Bibliographic record
Abstract
Purpose Multi‐b diffusion‐weighted hyperpolarized inhaled‐gas MRI provides imaging biomarkers of terminal airspace enlargement including ADC and mean linear intercept (Lm), but clinical translation has been limited because image acquisition requires relatively long or multiple breath‐holds that are not well‐tolerated by patients. Therefore, we aimed to accelerate single breath‐hold 3D multi‐b diffusion‐weighted 129Xe MRI, using k‐space undersampling in imaging direction using a different undersampling pattern for different b‐values combined with the stretched exponential model to generate maps of ventilation, apparent transverse relaxation time constant ( ), ADC, and Lm values in a single, short breath‐hold; accelerated and non‐accelerated measurements were directly compared. Methods We evaluated multi‐b (0, 12, 20, 30, and 45.5 s/cm2) diffusion‐weighted 129Xe /ADC/morphometry estimates using acceleration factor (AF = 1 and 7) and multi‐breath sampling in 3 volunteers (HV), and 6 participants with alpha‐1 antitrypsin deficiency (AATD). Results For the HV subgroup, mean differences of 5%, 2%, and 8% were observed between fully sampled and undersampled k‐space for ADC, Lm, and values, respectively. For the AATD subgroup, mean differences were 9%, 6%, and 12% between fully sampled and undersampled k‐space for ADC, Lm and values, respectively. Although mean differences of 1% and 4.5% were observed between accelerated and multi‐breath sampled ADC and Lm values, respectively, mean ADC/Lm estimates were not significantly different from corresponding mean ADCM/LmM or mean ADCA/LmA estimates (all P > 0.60 , A = undersampled and M = multi‐breath sampled). Conclusions Accelerated multi‐b diffusion‐weighted 129Xe MRI is feasible at AF = 7 for generating pulmonary ADC and Lm in AATD and normal lung.
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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) | 0.000 | 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".