Visualization of Rib and Diaphragm Motion in an Anaesthetized Mouse by Live Animal Synchrotron Imaging
Bibliographic record
Abstract
Pulmonary research is challenging because of the lack of visualization of lung airspace by commonly used imaging modalities. Cyclic breathing and motion artifacts due to superimposed ribs and cardiac motion further confound the interpretation of lung imaging data. This is partly responsible for little progress in drug therapy or intervention for many lung diseases such as acute respiratory distress syndrome, asthma, chronic obstructive pulmonary disease, cystic fibrosis, and lung cancer. Synchrotron sources can provide soft tissue contrast not available with conventional technologies by making use of phase contrast. Given the advantage of increased flux and narrow energy-range beams of collimated x-rays, there remains challenges in data analysis due to motion artifacts and lack of supporting image analysis algorithms for the correction of them. Therefore, we performed motion analysis of ribs and diaphragm for potential longitudinal functional lung imaging. The system utilized custom x-ray optics at the Canadian Light Source, for full field mouse imaging at 30 frames per second, on the biomedical beamline. This study presents a motion analysis of ribs and diaphragm of a spontaneously breathing anaesthetized mouse. To our knowledge this is the first temporal image analysis done in a spontaneously breathing individual without the aid of ventilation or extensive transducers. This is a feasibility study for future imaging of animal models of pulmonary diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".