Real time digital signal processing implementation for an APD-based PET scanner with phoswich detectors
Bibliographic record
Abstract
Recent progress in advanced digital signal processing provides an opportunity to expand the computation power required for real time extraction of event characteristics in avalanche photodiode (APD)-based Positron Emission Tomography (PET) scanners. These developments are made possible by a highly parallel data acquisition (DAQ) system based on an integrated analog front-end and a high-speed fully digital signal processing section that directly samples the output of each preamplifier with a free-running, off-the-shelf, 45-MHz analog-to-digital converter that feeds the sampled data into a field programmable gate array (FPGA) VirtexII PRO from Xilinx. This FPGA features /spl sim/ 31 000 logic cells and two PowerPC processors, which allows up to 64 channels to be processed simultaneously. Each channel has its own digital signal processing chain including a trigger, a baseline restorer and a timestamp algorithm. Various timestamp algorithms have been tested so far, achieving a coincidence timing resolution of 3.2-ns full-width at half-maximum (FWHM) for APD coupled to Lutetium Oxyorthosilicate (APD-LSO) and 11.4-ns FWHM for APD coupled to Bismuth Germanium Oxide (APD-BGO) detectors, respectively. Channels are then multiplexed into a DSP processor from Texas Instruments for crystal identification by an ARMAX recursive algorithm borrowed from identification and vector quantization theory. The system can sustain an event rate of 10 000 events/s/channel without electronic dead time.
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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 | 0.000 | 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) | 0.013 | 0.003 |
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".