Slit-slat collimator geometrical calibration for a PET/SPECT dual modality animal scanner
Bibliographic record
Abstract
We developed a cost-effective dual modality PET/SPECT imaging device based on an animal PET scanner. To achieve a large axial field of view (FOV) in the SPECT imaging mode, a slit-slat collimator insert was used. The objective of this work was to assess a method that used the PET imaging capability of the scanner to calibrate the geometrical parameters of the slit-slat collimator, including the slit aperture centers (SACs), axis of rotation (A OR), and slat center positions (SCPs). To calibrate the SAC and AOR values, the inner wedge surfaces of the slit apertures were painted with a18F solution. The slit cylinder was mounted in its SPECT setup and imaged in PET mode at multiple rotational positions. The SAC and AOR values were then estimated from the reconstructed PET images. To calibrate the SCP values, the slat assembly was mounted in its SPECT setup, with a18F capillary line source attached to its inner tube wall and along the scanner's axial direction, and imaged in PET mode. The axial sectional profile of the reconstructed PET image was used to estimate the SCPs. The calibrated geometrical parameters were used to generate the system matrix for SPECT image reconstruction. Phantom studies were performed. It was found that the proposed PET calibration method was easy to setup, fast to perform, and reliable.
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 | 0.004 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".