Bibliographic record
Abstract
In volumetric CT imaging of small animals, the breathing motion of the lungs during the image acquisition process results in inconsistent projection data being acquired. When reconstructed, these inconsistent data may produce images with reduced spatial resolution and image contrast. In order to minimize these effects, various approaches have been utilized to capture the respiratory signal of the animals under study and to only obtain CT data at specific moments in the respiratory cycle. These approaches typically utilize hardware-based physiological monitoring equipment in order to record the respiratory signal and either prospectively or retrospectively correlate this signal with acquired CT projection data. In this work, a new method of CT respiratory gating is described that does not rely on external physiological monitoring. Rather, determination of the respiratory phase of the animal is performed by postprocessing the acquired projection data. With this approach, any CT projection data can be respiratory gated with minimal effort. Validation of the method has been performed using a dynamic phantom and accuracy in tidal volumes determined to be within 16%. Rats and mice have been scanned and processed using the proposed method and compared to physiological-based measurement. With the proposed method, image quality is significantly improved in addition to providing quantitative information regarding tidal lung volumes.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".