Detector response correction for 3D PET using Bayesian modeling of the location Of interaction
Bibliographic record
Abstract
The unknown exact locations of the interaction (LOI) of photons within detector crystals is a major contribution to the loss of resolution in PET imaging. This effect is intensified with larger distances of the objects from the center of the field-of-view of the camera. In this work we propose a method that recover the loss of the resolution due to unknown LOI. The new method is an unique approach defined in Bayesian framework where the LOIs for two annihilation photons are modeled within the crystal volumes in which they were detected. LOI for each detected event is modeled independently, therefore that method works for binned and list-mode data. The approach was implemented for the minimum-mean-square-error (MMSE) estimator of the number of emissions per voxel. The Markov Chain Monte Carlo origin ensemble algorithm to find the estimator from the projection data was used. We performed computer simulations in 3D using Monte Carlo software of the data acquisitions and simulated Siemens Biograph PET scanner. We used small hot lesion phantom with nine 0.5 cm in diameter spheres placed in warm background. We found that compared to reconstructions with known LOIs for which we assumed that no loss of resolution occurred due to unknown LOI, the correction method proposed in this paper recovered the contrast for investigated object positioned in the center of the scanner. The contrast was only 80% recovered for the phantom positioned off-center due to suboptimal modeling of the LOIs. As expected, an increase in apparent noise in the reconstructed image was observed. This was especially evident for the background region. The method is a novel approach that can be used to improve the spatial resolution of images obtained by PET scanners. The method is directly applicable to time-of-flight PET.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".