Arterio-venous anastomoses in mice affect perfusion measurements with dynamic contrast enhanced CT
Bibliographic record
Abstract
Accurate measurement of perfusion with dynamic contrast enhanced CT requires an arterial input curve (AIC) uncontaminated by venous sources. Arterio-venous anastomoses (AVAs) are sources of contamination if contrast is injected intravenously. We seek to identify AVAs in mice and associated errors in perfusion measurements. Six transgenic mice with spontaneous prostate tumor were scanned with a micro-CT scanner (GE Healthcare (GE)) using a high resolution anatomical and a lower resolution perfusion protocol. For the anatomical protocol, a CT scan was performed during injection of an iodinated contrast agent (Hypaque) into a tail vein. Images covering the thoracic, abdominal and pelvic regions at an isotropic resolution of 175 microm were reconstructed and rendered in 3D to show the arterial and venous tree (Advantage Window, GE). For the perfusion protocol, each mouse was continuously scanned for 40 s and the contrast agent (Hypaque) was injected via a tail vein 5 s into scanning. Tumor images were reconstructed every second. Tumor blood flow (BF) and volume (BV) maps were calculated with CT perfusion software (GE) using AIC measured either from abdominal aorta (AA) or tail (caudal) artery (TA). In all mice, there was an AVA from the bifurcation of the inferior vena cava to the tail artery shunting venous blood and portion of the contrast agent injected into the tail vein into the TA. Contrast arrival time at the TA preceded that at the AA by 3.3 +/- 0.5 s (P < 0.05). Mean tumor BV and BF values calculated with AA versus TA were 10.0 +/- 1.8 versus 4.8 +/- 2.1 ml (100 g)(-1) (P < 0.05) and 108.8 +/- 26.5 versus 33.0 +/- 8.5 ml min(-1) 100 g(-1) (P < 0.05), respectively. AVA in the murine pelvic region can result in inaccurate and more variable measurements of pelvic organ/tissue perfusion when the tail artery is used as the AIC.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".