PET imaging of pulmonary embolism with 18F-Fluorogas and 64Cu-MAA
Bibliographic record
Abstract
1580 Objectives Lung embolism is evaluated by ventilation and perfusion scans which require two different tracers. The ventilation study is usually accomplished with an aerosol (99mTc-Technegas), while the accompanying perfusion assay is done with 99mTc-labeled albumin macroaggregates (99mTc-MAA). The aim of this study was to investigate the potential of 18F and 64Cu in these two preparations to yield 18F-Fluorogas and 64Cu-MAA for microPET imaging in rats. Methods Prior to the pulmonary PET imaging sequence, blood sample is taken from the rat and heated to form cloths. These cloths were injected into the rat via the jugular vein to produce embolism. 18F-Fluorogas (37-50MBq/0.1 mL) was generated using [18F]-NaF and a commercially available generator (Technegas generator; Cyclomedica, Sydney, Australia). After 5 min of breathing, the rat is positioned in a LabPET scanner and a dynamic image is acquired for 30 min. 64Cu-MAA were prepared by direct addition of [64Cu]-Cu(OAc)2 to MAA particles (4-8 millions, from a commercial Draximage MAA kit), followed by a 15min incubation at 75 °C in saline. Around 20MBq of 64Cu-MAA were injected to the rat by the tail vein and a 45 min dynamic image was acquired. Results 18F-Fluorogas particles reached the periphery of the lungs with almost no deposit in the respiratory tract and the distribution of 18F in the lungs was relatively uniform in healthy and embolized rats. The biological half-life of this tracer is about 10-15 min. The distribution of 64Cu-MAA was relatively uniform in lungs of healthy rats, whereas a perfusion defect was apparent in the upper posterior segment lobe of the embolized rat. Conclusions 18F-Fluorogas and 64Cu-MAA in healthy and embolized rats confirmed the potential of these tracers for the detection of pulmonary embolism by PET imaging
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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 | 0.000 | 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) | 0.001 | 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".