A combined time‐of‐flight and depth‐of‐interaction detector for total‐body positron emission tomography
Bibliographic record
Abstract
PURPOSE: In support of a project to build a total-body PET scanner with an axial field-of-view of 2 m, the authors are developing simple, cost-effective block detectors with combined time-of-flight (TOF) and depth-of-interaction (DOI) capabilities. METHODS: This work focuses on investigating the potential of phosphor-coated crystals with conventional PMT-based block detector readout to provide DOI information while preserving timing resolution. The authors explored a variety of phosphor-coating configurations with single crystals and crystal arrays. Several pulse shape discrimination techniques were investigated, including decay time, delayed charge integration (DCI), and average signal shapes. RESULTS: Pulse shape discrimination based on DCI provided the lowest DOI positioning error: 2 mm DOI positioning error was obtained with single phosphor-coated crystals while 3-3.5 mm DOI error was measured with the block detector module. Minimal timing resolution degradation was observed with single phosphor-coated crystals compared to uncoated crystals, and a timing resolution of 442 ps was obtained with phosphor-coated crystals in the block detector compared to 404 ps without phosphor coating. Flood maps showed a slight degradation in crystal resolvability with phosphor-coated crystals; however, all crystals could be resolved. Energy resolution was degraded by 3%-7% with phosphor-coated crystals compared to uncoated crystals. CONCLUSIONS: These results demonstrate the feasibility of obtaining TOF-DOI capabilities with simple block detector readout using phosphor-coated crystals.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".