Pharmacokinetic modeling of dynamic contrast‐enhanced MRI using a reference region and input function tail
Bibliographic record
Abstract
Purpose Quantitative analysis of dynamic contrast‐enhanced MRI (DCE‐MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. Methods RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5‐30 s) and noise levels (σ = 0.01‐0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient‐specific AIF tails, and another assuming a literature‐based AIF tail. Results RRIFT estimated the transfer constant and interstitial volume with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of had a median and interquartile range of −9 ± 45% with the ETM and −2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for , 0.96 for , and 0.73 for the plasma volume using a measured AIF tail. With the literature‐based AIF tail, the CCC was 0.89 for , 0.93 for and 0.78 for . Conclusions Quantitative DCE‐MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".