Investigation and optimization of parameter accuracy in dynamic contrast‐enhanced MRI
Bibliographic record
Abstract
PURPOSE: To present a modified pharmacokinetic model for improved parameter accuracy and to investigate the influence of an inaccurate arterial input function (AIF) on dynamic contrast-enhanced (DCE)-MRI parameter estimates of the transfer constant (Ktrans), blood volume (vp), and interstitial volume (ve). MATERIALS AND METHODS: Tissue uptake curves were simulated over a large range of physiological values and analyzed for different AIF measurement errors and temporal resolutions. The AIF measurement was assumed to be inaccurate in the bolus amplitude (rapid sampling) or susceptible to unknown temporal offsets (slow sampling with biexponential decay fit). RESULTS: The modified model adequately reduces errors in parameter estimates arising from transit time effects. An error in the AIF bolus amplitude results in an inversely proportional error in Ktrans and vp; ve remains robust. More consistent error in Ktrans (approximately 20% underestimation) was obtained using a biexponential AIF, at the expense of severely underestimating vp. CONCLUSION: While an accurate, high temporal resolution AIF is essential for estimating vp, a biexponential AIF acquired at low temporal resolution (<20 seconds) provides robust estimates of ve and results in a Ktrans underestimation comparable to that from a 25% error in the initial AIF bolus amplitude.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".