Printed sources for positron emission tomography (PET)
Bibliographic record
Abstract
We have developed a method that allows manufacturing of /sup 18/F radioactive printed sources using a standard ink-jet printer. Although previously used in printing and imaging single gamma emitter sources, such techniques have not been, to our knowledge, applied to the manufacturing of positron emitting sources. The added complication in the latter instance is a nonzero positron range and, thus, the need for some attenuating material surrounding the positron emitting atoms. The point sources were first imaged on a phosphor imager and then scanned on three different tomographs (Siemens/CTI ECAT 953B, CPS high-resolution research tomograph (HRRT) and Concorde microPET R4) to measure their point spread functions (PSFs). Where appropriate, the resolution agrees with published values. A comparison of the full width and tenth width half maxima of the point source profiles obtained with and without additional attenuating material showed no effect of the additional attenuation material on their values. The presence of the attenuating material however increased the number of counts in the point source image several fold due to a larger fraction of the positrons annihilating in the region close to the printed source. The results show that printed sources either on paper alone or on paper sandwiched between some additional attenuating material provide a practical means to obtain positron emitting sources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".