PET-based geometrical calibration of a pinhole SPECT add-on for an animal PET scanner
Bibliographic record
Abstract
We developed SPECT imaging capability on an animal PET scanner to provide a cost effective option for animal SPECT imaging. The SPECT add-on sub-system was enabled by mechanically integrating a multiple-pinhole collimator in the PET detector ring. This study introduces a method to calibrate the geometrical parameters of the SPECT add-on using the PET imaging capability of the scanner. The proposed PET imaging-based calibration method consists of two steps: (1) paint the pinhole apertures of the collimator with a positron emitting radioactive solution; and (2) image the collimator inside the scanner in PET mode. The geometrical parameters of the multi-pinhole SPECT add-on can then be derived directly from a set of PET images by simple linear calculation and used in defining the SPECT system. The method was compared to our implementation of a SPECT calibration approach with model-based fitting of SPECT projection data. The procedure for carrying out the PET imaging-based calibration method is simpler and faster than that of our implementation of the SPECT model-based calibration method. Since it does not require model fitting, the uniqueness of the calibration result is warranted. Better quality SPECT images were reconstructed using the PET-derived calibration parameters rather than our implementation of the SPECT model-based calibration parameters. We conclude that the proposed PET imaging-based calibration method provides a highly effective means for enabling SPECT imaging on a PET scanner.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".