Quantifying the effects of defective block detectors in a 3D whole body pet camera
Bibliographic record
Abstract
A comparison study was conducted in order to assess the image quality of a clinical 3D whole body PET scanner (Siemens Biograph 16 HiRez) in the condition of failure of one or more block detectors. A data set was acquired using the NEMA image quality phantom when all detectors were functioning normally. The ratio of the activity in the four smallest spheres to the background region was 8.27:1. Defective blocks were then simulated by zeroing the appropriate lines of response in the sinograms. Eight different combinations of defects are considered ranging from the case of no defect up to a complete bucket failure (12 blocks). Images were reconstructed with both OSEM and FBP using the manufacturer's software. The images were examined both qualitatively and according to the NEMA NU 2-2001 protocol for contrast, variability, and residual error. The results show that despite very visible artefacts appearing in the images the NEMA contrast analysis was very similar for all defect cases. The variability increased for all cases with simulated defective blocks. The contrast results demonstrate that a method of qualitatively evaluating the images is required in addition to the quantitative analysis. The preliminary data examined in this study suggest that data acquired when there are two defective blocks in the system might still produce clinically useable images.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".