Arterial input functions determined from MR signal magnitude and phase for quantitative dynamic contrast‐enhanced MRI in the human pelvis
Bibliographic record
Abstract
Dynamic contrast-enhanced (DCE) MRI is often used to measure the transfer constant (Ktrans) and distribution volume (ve) in pelvic tumors. For optimal accuracy and reproducibility, one must quantify the arterial input function (AIF). Unfortunately, this is challenging due to inflow and signal saturation. A potential solution is to use MR signal phase (ϕ), which is relatively unaffected by these factors. We hypothesized that phase-derived AIFs (AIFϕ) would provide more reproducible Ktrans and ve values than magnitude-derived AIFs (AIF|S|). We tested this in 27 prostate dynamic contrast-enhanced MRI studies (echo time=2.56 ms, temporal resolution=13.5 s), using muscle as a standard. AIFϕ peak amplitude varied much less as a function of measurement location (inferior-superior) than AIF|S| (5.6±0.6 mM vs. 2.6±1.5 mM), likely as a result of ϕ inflow insensitivity. However, our main hypothesis was not confirmed. The best AIF|S| provided similar reproducibility versus AIFϕ (interpatient muscle Ktrans=0.039±0.021 min(-1) vs. 0.037±0.025 min(-1), ve=0.090±0.041 vs. 0.062±0.022, respectively).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".