Incorporating contrast agent diffusion into the analysis of DCE‐MRI data
Bibliographic record
Abstract
Standard two-compartment pharmacokinetic models that describe the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time course of gadolinium diethylenetriamine pentaacetic acid (Gd-DTPA) concentration in a tissue do not account for the passive diffusion of contrast agent (CA) from a well-perfused to a less vascularized region. Even when the arterial input function (AIF) is perfectly known, the standard Tofts model returns inaccurate values of K(trans) (mean absolute relative difference [ARD] of 43%) from realistic simulated data where a well-defined delineation exists between a well-perfused and a poorly vascularized region. This contribution proposes a diffusion-perfusion (DP) model in which diffusion of a low molecular weight CA is incorporated in the standard two-compartment Tofts model. The proposed DP model reliably retrieved the values of K(trans) and v(e) (mean ARD of 16% and 17%, respectively) from simulated data. On mouse adenocarcinoma xenograft data showing evidence of CA diffusion, the standard model returned unphysical values of v(e) in the tumor core whereas the proposed DP model found values that were in the physical range (0 < v(e) < 1) throughout the tissue. In addition, K(trans) distributions from the DP model more closely corresponded to the observed sharp delineation between highly and poorly perfused areas observed in the mouse tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".