Three‐dimensional imaging of the mouse heart and vasculature using micro‐CT and whole‐body perfusion of iodine or phosphotungstic acid
Bibliographic record
Abstract
Recent studies have investigated histological staining compounds as micro-computed tomography (micro-CT) contrast agents, delivered by soaking tissue specimens in stain and relying on passive diffusion for agent uptake. This study describes a perfusion approach using iodine or phosphotungstic acid (PTA) stains, delivered to an intact mouse, to capitalize on the microvasculature as a delivery conduit for parenchymal staining and direct contact for staining artery walls. Twelve C57BL/6 mice, arterially perfused with either 25% Lugol's solution or 5% PTA solution were scanned intact and reconstructed with 26 µm isotropic voxels. The animals were fixed and the heart and surrounding vessels were excised, embedded and scanned; isolated heart images were reconstructed with 13 µm isotropic voxels. Myocardial enhancement and artery diameters were measured. Both stains successfully enhanced the myocardium and vessel walls. Interestingly, Lugol's solution provided a significantly higher enhancement of the myocardium than PTA [2502 ± 437 vs 656 ± 178 Hounsfield units (HU); p < 0.0001], delineating myofiber architecture and orientation. There was no significant difference in vessel wall enhancement (Lugol's, 1036 ± 635 HU; PTA, 738 ± 124 HU; p = 0.29), but coronary arteries were more effectively segmented from the PTA-stained hearts, enabling segmented imaging of fifth- order coronary artery branches. The combination of whole mouse perfusion delivery and use of heavy metal-containing stains affords high-resolution imaging of the mouse heart and vasculature by micro-CT. The differential imaging patterns of Lugol's- and PTA-stained tissues reveals new opportunities for micro-analyses of cardiac and vascular tissues.
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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 | 0.001 |
| 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) | 0.001 | 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".