Comparison of a reference region model with direct measurement of an AIF in the analysis of DCE‐MRI data
Bibliographic record
Abstract
Models have been developed for analyzing dynamic contrast-enhanced (DCE)-MRI data that do not require measurements of the arterial input function (AIF). In this study, experimental results obtained from a reference region (RR) analysis are compared with results of an AIF analysis in the same set of five animals (four imaged twice, yielding nine data sets), returning estimates of the volume transfer constant (Ktrans) and the extravascular extracellular volume fraction (ve). Student's t-test values for comparisons of Ktrans and ve between the two models were 0.14 (P=0.88) and 0.85 (P>0.4), respectively (where the high P-values indicate no significant difference between values derived from the two models). Linear regression analysis indicated there was a correlation between Ktrans extracted by the two methods: r2=0.80, P=0.001 (where the low P-value indicates a significant linear correlation). For ve there was no such correlation (r2=0.02). The mean (absolute) percent difference between the models was 22.0% for Ktrans and 28.1% for ve. However, the RR parameter values were much less precise than the AIF method. The mean SDs for Ktrans and ve for the RR analysis were 0.024 min-1 and 0.06, respectively, vs. 0.002 min-1 and 0.03 for AIF analysis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".