Micro-CT imaging of rat lung ventilation using continuous image acquisition during xenon gas contrast enhancement
Bibliographic record
Abstract
We measured ventilation (V) in seven anesthetized, mechanically ventilated, supine Wistar rats. Images of the whole lung were continuously acquired using a dynamic, flat-panel volumetric micro-computed tomography (micro-CT) scanner during ventilation with a xenon/oxygen (Xe-O(2)) gas mixture. Forty time-resolved volumes consisting of eighty 0.45-mm-thick slices (covering the entire lung) were acquired in 40 s, using a gantry rotation rate of one rotation per second. The animals were ventilated at a respiratory rate of 60 breaths/min, matching the gantry rotation rate, and imaged without suspending ventilation. A previously published theoretical model was modified slightly and used to calculate the whole lung ventilation from volumes of interest generated by seeded region growing. Linear regression of calculated whole lung ventilation volumes vs. expected tidal volumes yielded a slope of 1.12 +/- 0.11 (slope +/- SE) and a y-intercept of -1.56 +/- 0.42 ml (y-intercept +/- SE) with 95% confidence intervals of 0.83 to 1.40 and -2.6 to -0.5 ml, respectively. The same model was used to calculate the regional ventilation in axial slices for each animal. Voxels were fit to the model to yield a map of V, which displayed an anterior/posterior gravitational gradient of (-3.9 +/- 1.8) x 10(-6) mlxs(-1)xcm(-1) for slices immediately superior to the diaphragm and (-6.0 +/- 2.4) x 10(-6) mlxs(-1)xcm(-1) for slices at the midlevel of the heart (mean +/- SD). Thus continuous Xe-enhanced computed tomography enables the noninvasive determination of regional V with the temporal and spatial resolution necessary for rats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".