Time determination of BGO-APD detectors by digital signal processing for positron emission tomography
Bibliographic record
Abstract
Coincidence timing resolution in positron emission tomography (PET) can be improved by replacing fast analog pulse shaping and constant fraction discriminator (CFD) with fully digital signal processing. This can be achieved by digitizing the signal from individual detectors using 100-MHz 8-bit analog-to-digital converters (ADC) and processing the data in field programmable gate arrays (FPGA). Various digital implementation of filters and baseline restorers have been combined with numerical least mean square fit to the data to extract the time of interaction and the energy deposited in BGOAPD detectors. Using the same detector and pre-amplifier, a time resolution of 7.2 ns was obtained with digital techniques, as compare to 12.7 ns with the conventional analog method. By reducing the BGO-BGO coincidence time window by more than /spl sim/40 %, an equivalent reduction of the rate of random events can be obtained, which would improve image SNR significantly at high counting rate in a BGO-APD PET scanner. The proposed digital techniques can be readily adapted to other faster detectors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".