Rapid measurement of arterial input function in mouse tail from projection phases
Bibliographic record
Abstract
PURPOSE: To measure the arterial input function (AIF) in a mouse tail at high temporal resolution with signal phase of MR projections. METHODS: The technique involves the acquisition of one 2D image before injection, followed by a series of projections before, during, and after contrast injection. Differences in the signal phase, relative to the mean preinjection phase, were calculated and converted into a concentration of Gd. RESULTS: An AIF with a temporal resolution of 100 ms was measured and verified with colorimetry (in a flow phantom) and mass spectrometry analysis (in vivo). The projection-based AIF is expected to better represent the rapid contrast kinetics in the blood following injection, thus improving the accuracy of quantitative dynamic contrast-enhanced-MRI analysis. Colorimetry experiments confirmed that signal phase is preferred over magnitude for a precise determination of an AIF. In-vivo experiments demonstrate the feasibility of our approach in mice. CONCLUSION: AIFs can be measured quickly and precisely using phase from projections. Phase data are sensitive to the flow velocity; but this sensitivity is significantly reduced when flow compensation was used.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".