External delay and dispersion correction of automatically sampled arterial blood with dual flow rates
Bibliographic record
Abstract
Abstract Objective. Arterial sampling for PET imaging often involves continuously measuring the radiotracer activity concentration in blood using an automatic blood sampling system (ABSS). We proposed and validated an external delay and dispersion correction procedure needed when a change in flow rate occurs during data acquisition. We also measured the external dispersion constant of [11C]CURB, [18F]FDG, [18F]FEPPA, and [18F]SynVesT-1. Approach. External delay and dispersion constants were measured for the flow rates of 350, 300, 180, and 150 ml h−1, using 1-minute-long rectangular inputs (n = 10; 18F-fluoride in saline). Resulting constants were used to validate the external delay and dispersion corrections (n = 6; 18F-fluoride in saline; flow rate change: 350 to 150 ml h−1 and 300 to 180 ml h−1); constants were modelled to transition linearly between flow rates. Corrected curves were assessed using the percent area-under-the-curve (AUC) ratio and a modified model selection criterion (MSC). External delay and dispersion constants were measured for various radiotracers using a blood analog (i.e., similar viscoelastic properties). Main results. ABSS outputs were successfully corrected for external delay and dispersion using our proposed method accounting for a change in flow rate. AUC ratio reduced from ∼10% for the uncorrected 350–150 ml h−1 output (∼6% for the 300–180 ml h−1) to < 1% after correction when compared to true input (511 keV energy window); approx. 5-fold increase in MSC. Assuming an internal dispersion constant of 5 s, the dispersion constant (internal + external) for [11C]CURB, [18F]FDG, [18F]FEPPA, and [18F]SynVesT-1 was 13, 9, 16, and 10 s, respectively. Significance. This study presented an external delay and dispersion correction procedure needed when a change in flow rate occurs during ABSS data acquisition. Additionally, this is the first study to measure the external delay and dispersion constants using a blood analog solution, a suitable alternative to blood when estimating external dispersion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".