The Architecture of LabTEP, a Small Animal APD-Based Digital PET Scanner
Bibliographic record
Abstract
The highly multiplexed, integrated analog processing front-end of current PET scanners yields high accuracy for timing and crystal identification, but also adds significant dead time and offers little flexibility for improvement. A new fully digital APD-based scanner architecture is proposed wherein nuclear pulses are sampled directly at the output of the charge sensitive preamplifier with one free-running ADC per channel. This approach offers the opportunity to explore new digital signal processing algorithms borrowed to command and control theory, as well as advanced heuristics such as neural networks. The analog front-end consists of a dedicated 0.18-mum, 16-channel CMOS charge sensitive preamplifier. Digitization is performed with off-the-shelf dual 8-bit 45-MHz analog-to-digital converters. Digital processing is concentrated in a dual processor FPGA and a digital signal processor (DSP), which can process the data from up to 64 parallel channels with no dead time. The FPGA deals with the initial signal analysis for energy measurement and time stamping, while crystal identification is deferred to the DSP running computation-intensive auto-regressive algorithms. The entire system is controlled serially through a Firewire link by a graphic user interface. The initial LabPETtrade implementation of the system is a dedicated small animal scanner holding up to 3072 APD channels at an averaged count rate of up to 10 000 events/s each
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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 | 0.000 | 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".