Maximum likelihood positioning in the scintillation camera using depth of interaction
Bibliographic record
Abstract
The depth of interaction (DOI) in the scintillation crystal of a gamma camera is modifying the response of each photomultiplier, therefore introducing imprecision in the evaluation of both event energy and position. To compensate for these errors, an iterative 3-D maximum likelihood positioning algorithm (x, y, and DOI) was developed. An analytical calculation of the exact solid angle function yields the DOI, which is used to reevaluate the event energy, thus compensating for that portion of light which die not attain the photodetectors. The method was tested on a Monte Carlo simulator, with special attention given to noise modeling. Two models were developed, the first considering only the geometric aspects of the camera and used for comparison, and the second describing a more realistic camera environment. Different signal-to-noise ratios were used to test the algorithm. As an indication of the performance, quasi-perfect positioning was achieved with the technique using the geometric model while the 2-D approach still produces close to 1-mm deviation in some points. Energy evaluation is greatly improved, offering a way to stabilize camera performance over the entire energy spectrum.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".