Resolution recovery for Compton camera using origin ensemble algorithm
Bibliographic record
Abstract
Spatial resolution that can be achieved by a Compton camera (CC) is limited by finite energy and spatial resolutions of camera's detectors and by Doppler broadening. In principle, modeling of these effects in image reconstruction would allow for at least partial recovery of the lost resolution. Unfortunately, a substantial increase in computing time precludes practical implementations of such modeling when using standard iterative reconstruction algorithms, such as Maximum Likelihood Expectation Maximization (MLEM). In this work we propose a new method to model resolution degrading effects using origin ensemble (OE) image reconstruction algorithm. The basic principle of the resolution recovery with OE is the stochastic modeling of distributions of deposited energies and locations of photon interactions within the detectors. To test the developed algorithm we designed a high sensitivity CC aimed at small animal/mammography imaging, with scatter and absorption detectors replaced by a single thick, multi-layer CZT detector. The simulated radioactive source consisted of four 3 mm in diameter hot spheres placed in a warm background. Single detection of 511 keV photons was modeled. Results show that proposed method substantially improved not only image resolution but also quantitative accuracy of the reconstructed activity distributions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".