Dynamic contrast‐enhanced MRI in mice at high field: Estimation of the arterial input function can be achieved by phase imaging
Bibliographic record
Abstract
PURPOSE: Quantitative dynamic contrast-enhanced MRI requires an accurate arterial input function (AIF). At high field, increased susceptibility effects and decreased longitudinal relaxivity of contrast agents lead to predominant T2* effects in blood vessels, producing a dip in signal during passage of the contrast agent bolus. This study determined phase-derived AIFs in mice at 11.7 T. METHODS: AIFs were measured in aorta/vena cava for five FBV/N mice and in iliac arteries/veins for five NMRI mice with a fast low angle shot sequence, simultaneously with tumor imaging (temporal resolution: 1.19 s). Gadoterate was injected into the tail vein as a bolus (0.286 mmol Gd/kg). An in vitro study was also performed to calculate the relationship between ΔΦ and gadolinium concentration. RESULTS: The phantom system confirmed the linear relationship between measured ΔΦ and gadolinium concentration. In vivo, a dip in arterial magnitude signal made it impossible to quantify the AIF. With phase imaging, a clear quantifiable bolus peak was obtained; peak measured concentration in plasma was 4.9 ± 0.9 mM for FBV/N mice and 8.0 ± 0.6 mM for NMRI mice, close to the expected concentration of 6.8 mM. CONCLUSION: Phase imaging seems to be an appropriate means to measure the AIF of mice at high field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".