A perfusion procedure for imaging of the mouse cerebral vasculature by X-ray micro-CT
Bibliographic record
Abstract
BACKGROUND: Micro-CT is a novel X-ray imaging modality which can provide 3D high resolution images of the vascular network filled with contrast agent. The cerebrovascular system is a complex anatomical structure that can be imaged with contrast enhanced micro-CT. However, the morphology of the cerebrovasculature and many circulatory anastomosis in the brain result in high variations in the extent of contrast agent filling in the blood vessels and as a result, the vasculature of different subjects appear differently in the acquired images. Specifically, the posterior circulation is not consistently perfused with the contrast agent in many brain specimens and thus, many major vessels that perfuse blood to the midbrain and hindbrain are not visible in the micro-CT images acquired from these samples. NEW METHOD: In this paper, we present a modified surgical procedure of cerebral vasculature perfusion through the left ventricle with Microfil contrast agent, in order to achieve a more uniform perfusion of blood vessels throughout the brain and as a result, more consistent images of the cerebrovasculature. Our method consists of filling the posterior cerebral circulation with contrast agent, followed by the perfusion of the whole cerebrovasculature. RESULTS: Our histological results show that over 90% of the vessels in the entire brain, including the cerebellum, were filled with contrast agent. COMPARISON WITH EXISTING METHOD: Our results show that the new technique of sample perfusion decreases the variability of the posterior circulation in the cerebellum in micro-CT images by 6.9%. CONCLUSIONS: This new technique of sample preparation improves the quality of cerebrovascular 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".