Dynamic Imaging on the High Resolution Research Tomograph (HRRT): Non-Human Primate Studies
Bibliographic record
Abstract
The high resolution research tomography (HRRT) is currently the most complex human brain scanner due to its ability to detect the gamma depth of interaction, its octagonal geometry, and the large number of crystals (119,808) leading to approximately 4.5 /spl times/ 10/sup 9/ possible lines of response (LORs). Reconstruction of dynamic studies on this scanner is particularly challenging due to the dynamic range of both, number of acquired events per frame and acquisition count rates. Some artifacts have been observed with phantom studies: here we evaluate their impact on time activity curves (TACs) and binding potential (BP) values in realistic scanning situations with the ultimate goal of defining an efficient and accurate image reconstruction protocol. Non-human primate studies were used for this purpose. We compared TACs and BPs obtained from images reconstructed with three different reconstruction algorithms, two different axial spanning configurations and detector normalization factors obtained from two different data sets. We also compared BP values obtained from scans of the same animal performed on the Siemens ECAT 963B and the HRRT under identical conditions. The statistical reconstruction methods produced nearly identical results and the impact of emission/normalization count rate mismatch was found to be effectively negligible. Likewise no image degradation due to increased axial spanning was observed. Data obtained from the analytical method were less robust and in general much more sensitive to noise, thus demonstrating a suboptimal performance of this algorithm. The BP values obtained with the HRRT were by approximately 50% higher compared to those obtained in the ECAT as a result of the increased resolution of this tomograph.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".