Design of a Real-Time FPGA-Based Data Acquisition Architecture for the LabPET II: An APD-Based Scanner Dedicated to Small Animal PET Imaging
Why this work is in the frame
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Bibliographic record
Abstract
The LabPET II detector block was designed to achieve submillimeter spatial resolution in small animal PET imaging. Each detection block consists of two arrays of 4 × 8 avalanche photodiodes (APD) individually coupled to an 8 × 8 scintillator array, to form 64 independent detectors with parallel readout channels. This new detection block entails an eightfold increase in pixel density compared to the LabPET I. A 64-channel mixed-signal application-specific integrated circuit (ASIC) was designed to extract relevant PET data in real time from the LabPET II detection blocks. In order to interface the ASICs forming the PET camera with the storage units, a real-time FPGA-based digital data acquisition (DAQ) system was designed. The DAQ system allows event harvesting, processing and transmission to a host computer for data storage as well as system programming and calibration. Real-time event processing embedded in the DAQ includes time trigger, energy computation using a time-over-threshold (TOT) conversion scheme, timing corrections, and event sorting trees. In the standard DAQ mode, a real-time coincidence engine analyzes events and only keeps relevant information to minimize data throughput and post-acquisition data processing. The architecture consists of three FPGA-based electronic layers wired through gigabit links: a Front-End layer extracts time and energy along with the pixel address, a custom Hub layer chronologically sorts incoming events, and a Coincidence engine matches coincident events and computes an estimate of the random events rate. Every FPGA in the different layers is accessible through an Ethernet link. The real-time digital architecture sustains the required throughput of ~ 111 million events/s for a ~ 37000-channel scanner configuration.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it