Fast Retrospectively Gated Quantitative Four-Dimensional (4D) Cardiac Micro Computed Tomography Imaging of Free-Breathing Mice
Bibliographic record
Abstract
OBJECTIVE: We sought to demonstrate retrospectively gated dynamic 3D cardiac micro computed tomography (CT) of free-breathing mice. MATERIALS AND METHODS: Five C57Bl6 mice were scanned using a cone-beam scanner with a slip-ring-mounted flat-panel detector. After the injection of an intravascular iodinated contrast agent, projection images were acquired over the course of 50 seconds, while the scanner rotated through 10 complete rotations. The mouse respiratory and electrocardiogram signals were recorder simultaneously with image acquisition. After acquisition, the projection images were retrospectively sorted into projections belonging to different cardiac time points, occurring only during expiration. RESULTS: Dynamic 3D cardiac images, with isotropic 150-microm voxel spacing, were reconstructed at 12-millisecond intervals throughout the cardiac cycle in all mice. The average ejection fraction and cardiac output were 58.2+/-4.6% and 11.4+/-1.3 mL/min, respectively. The measured entrance dose for the entire scan was 28 cGy. Repeat scans of the same animals showed that intrasubject variability was smaller than intersubject variability. CONCLUSIONS: We have developed a high-resolution micro computed tomography method for evaluating the cardiac function and morphology of free-breathing mice in acquisition times shorter than 1 minute.
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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) | 0.000 | 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) | 0.002 | 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".