A scale to measure MRI contrast agent sensitivity
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) provides superior resolution of anatomical features and the best soft tissue contrast, and is one of the predominant imaging modalities. With this technique, contrast agents are often used to aid discrimination by enhancing specific features. Over the years, a rich diversity of such agents has evolved and with that, so has a need to systematically sort contrast agents based on their efficiency, which directly determines sensitivity. Herein, we present a scale to rank MRI contrast agents. The scale is based on analytically determining the minimum detectable concentration of a contrast agent, and employing a ratiometric approach to standardize contrast efficiency to a benchmark contrast agent. We demonstrate the approach using several model contrast agents and compare the relative sensitivity of these agents for the first time. As the first universal metric of contrast agent sensitivity, this scale will be vital to easily assessing contrast agent efficiency and thus important to promoting use of some of the elegant and diverse contrast agents in research and clinical practice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".