Extended graphical model for analysis of dynamic contrast‐enhanced MRI
Bibliographic record
Abstract
Kinetic analysis with mathematical models has become increasingly important to quantify physiological parameters in computed tomography (CT), positron emission tomography (PET), and dynamic contrast-enhanced MRI (DCE-MRI). The modified Kety/Tofts model and the graphical (Patlak) model have been widely applied to DCE-MRI results in disease processes such as cancer, inflammation, and ischemia. In this article, an intermediate model between the modified Kety/Tofts and Patlak models is derived from a mathematical expansion of the modified Kety/Tofts model. Simulations and an in vivo experiment involving DCE-MRI of carotid atherosclerosis were used to compare the new extended graphical model with the modified Kety/Tofts model and the Patlak model. In our simulated circumstances and the carotid artery application, we found that the extended graphical model exhibited lower noise sensitivity and provided more accurate estimates of the volume transfer constant (K(trans)) and fractional plasma volume (v(p)) than the modified Kety/Tofts model for DCE-MRI acquisitions of total duration less than 100-300 s, depending on kinetic parameters. In comparison with the Patlak model, we found that the extended graphical model exhibited 74.4-99.8% less bias in estimates of K(trans). Thus, the extended graphical model may allow kinetic modeling of DCE-MRI results with shortened data acquisition periods, without sacrificing accuracy in estimates of K(trans) and v(p).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".