A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results
Bibliographic record
Abstract
Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts-Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p ≪ 0.001). In poorly perfused (i.e., K(trans) ≤ 0.05 min(-1)) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low K(trans) values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".