<i>T</i><sub>1</sub> measurement of flowing blood and arterial input function determination for quantitative 3D <i>T</i><sub>1</sub>‐weighted DCE‐MRI
Bibliographic record
Abstract
PURPOSE: To propose a simple, accurate method for measuring T(1) in flowing blood and the arterial input function (AIF), and to evaluate the impact on dynamic contrast-enhanced MRI (DCE-MRI) quantification of pharmacokinetic parameters. MATERIALS AND METHODS: A total of 10 rabbits were scanned at 1.5 Tesla and administered a bolus of Gadomer. Preinjection T(1) and AIF measurements were acquired in the iliac arteries using a rapid three-dimensional (3D) spoiled gradient recalled echo (SPGR) approach. Correction was made for imperfect B(1) fields, in-flow, and partial volume effects. DCE-MRI parameters blood volume (v(b)) and endothelial transfer constant (K(trans)) in resting skeletal muscle were estimated from pharmacokinetic analysis using individually measured AIFs. Literature comparisons were made to assess accuracy. RESULTS: Blood T(1) was more accurate and precise after correction for B(1) and partial volume errors (1267 +/- 72 msec). Measured AIFs followed reported biexponential decay characteristics for Gadomer clearance in rabbits. Parameters v(b) (2.47 +/- 0.65%) and K(trans) (3.6 +/- 1.0 x 10(-3) minute(-1)) derived from AIFs based on corrected blood T(1)s were more reproducible and in better agreement with literature values. CONCLUSION: The proposed method enables accurate in vivo blood T(1) and AIF measurements and can be easily implemented in a range of DCE-MRI applications to improve both the accuracy and reproducibility of pharmacokinetic parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".