An investigation of radiation damage in rat lungs following dual-energy micro-CT imaging
Bibliographic record
Abstract
Abstract Dual-energy micro-CT imaging techniques have been developed to enable accurate identification and segmentation of different tissues. Using dual-energy techniques for thoracic imaging requires obtaining images at multiple respiratory or cardiac phases, and may require images obtained pre- and post-contrast enhancement for each energy. In this study, we investigated if the multiple images obtained during dual-energy imaging resulted in an x-ray dose sufficiently high to interfere with or mask symptoms of respiratory disease. We performed a dual-energy micro-CT study (5 images in a single session, with a cumulative entrance dose of 0.47 Gy) to image the thorax of healthy male Brown Norway rats at 8 weeks of age. Groups of 5 rats were euthanized at 1 day, 1, 2, 3, and 4-weeks post-exposure and the lungs were excised and examined by histology (H&E stained slides). Positive controls were exposed to an entrance dose of 1.5 Gy and euthanized at 2 weeks and negative controls were not exposed to x-rays. There was no evidence of alveolar damage or inflammation for any of the animals exposed to the dual-energy imaging session compared with the negative control group. Inflammation was evident for the positive controls. This study concludes that the dual-energy imaging protocol developed in this study does not contribute to lung tissue damage. For preclinical respiratory research, these results show that any inflammation and alveolar damage observed in the lungs would be attributed to the disease model under investigation, and not be affected by obtaining 3D dual-energy micro-CT images.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".