Derivation of the system matrix for an animal SPECT scanner with rotational collimator and stationary ring detector
Bibliographic record
Abstract
The aim of this work is to develop a cost-effective technology for performing SPECT imaging on an existing animal PET scanner. Two replaceable collimators attached to the PET ring detector to provide single-photon imaging capability are investigated: 1) a slit-slat collimator for large axial field of view and 2) a multi-pinhole collimator for high spatial resolution. The slit component of the slit-slat collimator and the pinhole collimator are rotated to acquire tomographic data sets. Every rotational step of the collimator forms a new detector system configuration, which requires a new system matrix and thus the overall system matrix increases linearly with the rotation steps. In this work, an efficient approach is first proposed for storing the system matrix. It reduces the matrix size by 98.5% in comparison to the original size if stored in direct form. To achieve a balance of accuracy and computational time, we investigate two ways to derive the system matrix: from Monte Carlo simulation which takes much longer time but is more accurate and from analytical calculation which is hundreds times faster but has reduced accuracy. The point spread functions calculated using both methods are compared and show general resemblance. The system matrices derived in both ways are used in reconstructing simulated and experimental data and have produced images with the expected resolution. The distinctive characteristics of the two methods make them useful at different stages of the system development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".