Effect of T<sub>1</sub> relaxation on ventilation mapping using hyperpolarized <sup>129</sup>Xe multiple breath wash‐out imaging
Bibliographic record
Abstract
Purpose To investigate the effect of incorporating T1 as a function of wash‐out breath number (T1(n)) on estimation of fractional ventilation (r) using hyperpolarized 129Xe multiple breath wash‐out (MBWO) imaging in rats. Methods MBWO imaging was performed in 8 healthy mechanically ventilated rats at several inter‐image delay times (τ) and tidal volumes (TV). r maps were calculated from the imaging data using a model of T1(n) (assuming that the longitudinal relaxation rate of 129Xe in the lung is directly proportional to pAO2) and compared to r maps obtained by assuming a fixed T1 measured before wash‐out breaths (r′). Results Fractional ventilation was overestimated by up to 19.3% when T1 was fixed. An inverse relationship between bias (Δr) and ventilation was observed at all τ and TV. Additionally, Δr significantly increased when TV was decreased (F statistic F(2,7) = 48.97, P < 10−4). Histograms from r′ maps were significantly more skewed toward lower values as compared to r histograms at all τ and TV (P < 0.05) except TV = Vdose – 1 mL. Conclusion Analysis of hyperpolarized 129Xe MBWO imaging using a model incorporating T1(n) corrects for an overestimating bias in the mapping of fractional ventilation in mechanically ventilated rats introduced by assuming a fixed T1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".