Respiratory-gated KES imaging of a rat model of acute lung injury at the Canadian Light Source
Bibliographic record
Abstract
In this study, contrast-enhanced X-ray tomographic imaging for monitoring and quantifying respiratory disease in preclinical rodent models is proposed. A K-edge imaging method has been developed at the Canadian Light Source to very accurately obtain measurements of the concentration of iodinated contrast agent in the pulmonary vasculature and inhaled xenon in the airspaces of rats. To compare the iodine and xenon concentration maps, a scout projection image was acquired to define the region of interest within the thorax for imaging and to ensure the same locations were imaged in each K-edge subtraction (KES) acquisition. A method for triggering image acquisition based on the real-time measurements of respiration was also developed to obtain images during end expiration when the lungs are stationary, in contrast to other previously published studies that alter the respiration to accommodate the image acquisition. In this study, images were obtained in mechanically ventilated animals using physiological parameters at the iodine K-edge in vivo and at the xenon K-edge post mortem (but still under mechanical ventilation). The imaging techniques were performed in healthy Brown Norway rats and in age-matched littermates that had an induced lung injury to demonstrate feasibility of the imaging procedures and the ability to correlate the lung injury and the quantitative measurements of contrast agent concentrations between the two KES images. The respiratory-gated KES imaging protocol can be easily adapted to image during any respiratory phase and is feasible for imaging disease models with compromised lung function.
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 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) | 0.000 | 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 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".