Arterial input function of an optical tracer for dynamic contrast enhanced imaging can be determined from pulse oximetry oxygen saturation measurements
Bibliographic record
Abstract
In many cases, kinetic modeling requires that the arterial input function (AIF)--the time-dependent arterial concentration of a tracer--be characterized. A straightforward method to measure the AIF of red and near-infrared optical dyes (e.g., indocyanine green) using a pulse oximeter is presented. The method is motivated by the ubiquity of pulse oximeters used in both preclinical and clinical applications, as well as the gap in currently available technologies to measure AIFs in small animals. The method is based on quantifying the interference that is observed in the derived arterial oxygen saturation (SaO₂) following a bolus injection of a light-absorbing dye. In other words, the change in SaO₂ can be converted into dye concentration knowing the chromophore-specific extinction coefficients, the true arterial oxygen saturation, and total hemoglobin concentration. A simple error analysis was performed to highlight potential limitations of the approach, and a validation of the method was conducted in rabbits by comparing the pulse oximetry method with the AIF acquired using a pulse dye densitometer. Considering that determining the AIF is required for performing quantitative tracer kinetics, this method provides a flexible tool for measuring the arterial dye concentration that could be used in a variety of applications.
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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".