Intramyocardial capillary blood volume estimated by whole-body CT: validation by micro-CT
Bibliographic record
Abstract
Fast CT has shown that myocardial perfusion (F) is related to myocardial intramuscular blood volume (Bv) as Bv=A*F+B*F1/2 where A,B are constant coefficients. The goal of this study was to estimate the range of diameters of the vessels that are represented by the A*F term. Pigs were placed in an Electron Beam CT (EBCT) scanner for a perfusion CT scan sequence over 40 seconds after an IV contrast agent injection. Intramyocardial blood volume (Bv) and flow (F) were calculated in a region of the myocardium perfused by the LAD. Coefficients A and B were estimated over the range of F=1-5ml/g/min. After the CT scan, the LAD was injected with Microfil(R) contrast agent following which the myocardium was scanned by micro-CT at 20μm, 4μm and 2.5 μm cubic voxel resolutions. The Bv of the intramyocardial vessels was calculated for diameter ranges d=0-5, 5-10, 10-15, 15-20μm, etc. EBCT-derived data were presented so that it could be directly compared the micro-CT data. The results indicated that the blood in vessels less than 10μm in lumen diameter occupied 0.27-0.42 of total intravascular blood volume, which is in good agreement with EBCT-based values 0.28-0.48 (R2 =0.96). We conclude that whole-body CT image data obtained during the passage of a bolus of IV contrast agent can provide a measure of the intramyocardial intracapillary blood volume.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".