Quantitative measurement of regional lung gas volume by synchrotron radiation computed tomography
Bibliographic record
Abstract
The aim of this study was to assess the feasibility of a novel respiration-gated spiral synchrotron radiation computed tomography (SRCT) technique for direct quantification of absolute regional lung volumes, using stable xenon (Xe) gas as an inhaled indicator. Spiral SRCT with K-edge subtraction using two monochromatic x-ray beams was used to visualize and directly quantify inhaled Xe concentrations and airspace volumes in three-dimensional (3D) reconstructed lung images. Volume measurements were validated using a hollow Xe-filled phantom. Spiral images spanning 49 mm in lung height were acquired following 60 breaths of an 80% Xe-20% O2 gas mixture, in two anaesthetized and mechanically ventilated rabbits at baseline and after histamine aerosol inhalation. Volumetric images of 20 mm lung sections were obtained at functional residual capacity (FRC) and at end-inspiration. 3D images showed large patchy filling defects in peripheral airways and alveoli following histamine provocation. Local specific lung compliance was calculated based on FRC/end-inspiration images in normal lung. This study demonstrates spiral SRCT as a new technique for direct determination of regional lung volume, offering possibilities for non-invasive investigation of regional lung function and mechanics, with a uniquely high spatial resolution. An example of non-uniform volume distribution in rabbit lung following histamine inhalation is presented.
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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 | 0.001 | 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".