Sci—Thur PM: YIS — 03: Design and Performance Evaluation of Detector Modules for Positron Emission Mammography Imagers
Bibliographic record
Abstract
Positron Emission Mammography (PEM) is a valuable molecular imaging technique used to visualize to both invasive and pre‐invasive lesions in the breast. While maintaining spatial resolution of 1–2 mm, a reduction of patient dose is required in order to use PEM in clinical metastatic cancer prevention studies. We are working to enable depth of interaction (DOI) measurements using a detector module design with photodetectors on either end of a scintillation crystal array to allow for increased detection efficiency. While typical dual‐ended readout designs couple one photodetector to either end of one crystal, we propose reducing readout requirements by enlarging the photodetectors such that one detector may readout one end of multiple scintillators. We simulated a dual‐ended detector module with photodetectors appropriately sized for 4 detectors to readout each end of a 3×3, 5×5, and 6×6 scintillation crystal array. Our work found that detector modules can accommodate up to 5×5 scintillators while achieving accurate crystal index identification and depth of interaction resolution of 1.33 mm. Based on this optimization, a detector module was assembled for both the single‐ and dual‐ended readout configurations using a 6×6 LYSO scintillation crystal array coupled to a 4×4 SiPM array. We successfully resolved all 36 scintillators in the single‐ended configuration verifying that large sized SiPM pixels may be used to accurately identify smaller pixelated scintillators. We are continuing this work for the dual‐ended configuration.
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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 | 0.000 |
| 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".