Scanning rodents on the High Resolution Research Tomograph (HRRT) with point spread function reconstruction: A feasibility study
Bibliographic record
Abstract
The ECAT High Resolution Research Tomograph (HRRT) is a dedicated human brain PET camera with a 6% absolute sensitivity and a (2.3mm)3spatial resolution, improving to (1.8mm)3when point spread function (PSF) resolution recovery algorithms are used. These values are very close to those of the dedicated small animal PET camera microPET FOCUS 120 (F120). The larger axial and transaxial FoV of the HRRT compared to the F120 allows in principle for whole body imaging of several rats at the same time thus potentially reducing scanning costs and time. In this study we investigate the feasibility of using the HRRT for small animal brain studies by comparing the tissue input binding potentials (BPND) obtained from scans of the same rats imaged on the F120 and on the HRRT. The animal experiments are complemented by phantom studies aimed at investigating noise properties relevant to the size of typical regions of interest used in rat brain image analysis. Our investigations show that (i) without PSF modelling the BPNDobtained from HRRT data are lower than those obtained on the F120 by 28%, (ii) with PSF modelling the BPNDobtained from HRRT data are 15% higher than those obtained on the F120, (iii) the reproducibility on the HRRT is 81% when PSF modelling is not used, lower than the F120 reproducibility (92%), and decreases even further with PSF modelling to 73%. In addition Gibbs type artefacts are clearly visible when PSF modelling is used. In summary, while the resolution achieved with PSF modelling might be comparable to that obtained with the F120, the clumpy noise structure coupled with the introduction of Gibbs type artefacts in the images reconstructed with this technique currently preclude the use of the HRRT for reliable small animal brain 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".