Temporal resolution and SNR requirements for accurate DCE‐MRI data analysis using the AATH model
Bibliographic record
Abstract
Dynamic contrast-enhanced MRI has been used in conjunction with tracer kinetics modeling in a wide range of tissues for treatment monitoring, oncology drug development, and investigation of disease processes. Accurate measurement of model parameters relies on acquiring data with high temporal resolution and low noise, particularly for models with large numbers of free parameters, such as the adiabatic approximation to the tissue homogeneity model for separate measurements of blood flow and vessel permeability. In this simulation study, accuracy of the adiabatic approximation to the tissue homogeneity model was investigated, examining the effects of temporal resolution, noise levels, and error in the measured arterial input function. A temporal resolution of 1.5 s and high SNR (noise sd = 0.05) were found to ensure minimal bias (<5%) in all four model parameters (extraction fraction, blood flow, mean transit time, and extravascular extracellular volume), and the sampling interval can be relaxed to 6 s, if the transit time need not be measured accurately (bias becomes >10%). A 10% error in the measured height of the arterial input function first pass peak resulted in an error of at most 10% in each model parameter.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".