TU‐G‐211‐02: Design and Evaluation of Dual‐Ended Detectors for PET Mammography
Bibliographic record
Abstract
Purpose: Positron emission mammography shows promise as a secondary breast screening technique to reduce the number of unnecessary biopsies. To reduce the dose to the patient, current PEM systems use depth of interaction (DOI) enabling detector modules. These detector designs however require a large number of channels per scintillator at high system cost and complexity. We propose a dual‐ended readout block detector module (DERBDM) design combining the high encoding ratio of Anger logic with the DOI measuring dual‐ended readout detector design to reduce the number of required channels while maintaining spatial resolution on the order of 2‐mm. Methods: A prototype DERBDM was constructed from 2 × 2 pixels with 3.4‐mm pitch from two SensL silicon photomultipliers arrays (SPMArray2‐A0), two 2‐mm thick glass light guides, and a 3 × 3 array of 2‐mm × 2‐mm × 20‐mm scintillators with 2.075‐mm pitch. The signal from the DERBDM was digitized using eight channels of a CAEN Systems 16‐channel 12‐bit 250‐MS/s waveform digitizer. The ability to identify crystal index, DOI resolution, and energy resolution were measured. Results: The DERBDM was found to resolve events in a flood field image and with energy‐based corrections clearly identifying the interacting scintillation crystals. After per‐crystal corrections were applied, DOI was resolved with 5‐mm FHWM resolution. Energy was corrected with crystal index‐ and DOI‐ dependant and found to discriminate the energy of events with a resolution of 20%. Conclusions: This work demonstrates the DERBDMˈs ability to increase encoding ratio at least 2‐fold from that of the dual‐ended readout design without sacrificing spatial resolution. It indicates that the DERBDM has the potential to meet the need for an affordable, high resolution, highly specific breast imaging system.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".