RatCAP: miniaturized head-mounted PET for conscious rodent brain imaging
Bibliographic record
Abstract
Anesthesia is currently required for positron emission tomography (PET) studies of the animal brain in order to eliminate motion artifacts. However, anesthesia profoundly affects the neurological state of the animal, complicating the interpretation of PET data. Furthermore, it precludes the use of PET to study the brain during normal behavior. The rat conscious animal PET tomograph (RatCAP) is designed to eliminate the need for anesthesia in rat brain studies. It is a miniaturized full-ring PET scanner that is attached directly to the head, imaging nearly the entire brain. RatCAP utilizes arrays of 2 mm /spl times/ 2 mm LSO crystals coupled to matching avalanche photodiode arrays, which are in turn read out by full custom integrated circuits. Principal challenges have been addressed considering the physical constraints on size, weight, and heat generation in addition to the usual requirements of small-animal PET, such as high spatial resolution in the presence of parallax error. A partial prototype has been constructed and preliminary measurements and optimization completed. Realistic Monte Carlo simulations have also been carried out to optimize system performance, which is predicted to be competitive with existing microPET systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".