Cellular‐interstitial water exchange and its effect on the determination of contrast agent concentration in vivo: Dynamic contrast‐enhanced MRI of human internal obturator muscle
Bibliographic record
Abstract
The purpose of this study was to assess the effects of cellular-interstitial water exchange on estimates of tracer kinetics parameters obtained using rapid dynamic contrast-enhanced (DCE) MRI. Data from the internal obturator muscle of six patients were examined using three models of water exchange: no exchange (NX), fast exchange limit (FXL), and intermediate rate (shutter-speed [SS]). In combination with additional multiple flip angle (FA) data, a full two-pool exchange model was also used. The results obtained using the NX model (transfer constant, K(trans) = 0.049 +/- 0.027 min(-1), apparent interstitial volume, v(e) = 0.14 +/- 0.04) were marginally higher than those obtained using the FXL model (K(trans) = 0.045 +/- 0.025 min(-1), v(e) = 0.13 +/- 0.04), but the error bars overlapped in two-thirds of these parameter estimate pairs. Estimates of K(trans) and v(e) obtained using the SS model exceeded those obtained using the NX model in half the patients, and many estimates, including all those of intracellular residence time of water, t(i), were imprecise. Results obtained using the full two-pool model fell between those obtained using FXL and NX models, and estimates of t(i) were also imprecise. The results suggest that data obtained using clinically relevant DCE-MRI are exchange-insensitive and unsuitable for the assessment of cellular-interstitial water exchange.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".