Comparison of two systems of multiple line sources for SPECT transmission scanning
Bibliographic record
Abstract
The authors have proposed a new design for a transmission source which uses a system of multiple parallel line sources. It does not require complicated hardware, it allows the activity distribution to be tailored to the shape of the human body, minimizes both the amount of activity being used and patient dose, and substantially reduces the problem of low counts in transmission scans which can arise when large patients are scanned. The authors have built and investigated two systems based on line sources, namely the Collimated Line Sources (CLS) (10 lines) and the Multiple Line Array (MLA) (20 lines). Since they use different source positions and collimations, the systems require different approaches to the data processing and map reconstruction. This study presents a comparison of the results of simulations and phantom experiments performed using these two systems. Qualitative and quantitative analyzes of the attenuation maps were performed. The results of the authors' tests showed that transmission maps obtained with multiple line sources are artifact free, have good uniformity, 2-3% accuracy, about 1-1.5 cm resolution, and acceptable levels of noise. Preliminary simultaneous emission/transmission studies using MLA system and involving both phantom and patients have demonstrated that these maps can be successfully used to correct for attenuation.
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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 | 0.003 | 0.008 |
| 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 | 0.002 | 0.001 |
| 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".