Digital SPAD scintillation detector simulation flow to evaluate and minimize real-time requirements
Bibliographic record
Abstract
Radiation detection used in positron emission tomography (PET) exploit the timing information to remove background noise and refine the position measurement through time-of-flight (TOF) information. In PET, very fine time resolution (in the order of 10 ps FWHM) would not only improve contrast in the image, but would also enable real-time image reconstruction without iterative or back-projected algorithms. The current performance limitations will be pushed off through the optimization of faster light emission mechanisms (prompts photons), after which the burden of timing resolution will fall to the readout optoelectronics. Digital SPAD arrays offer compelling possibilities to minimize timing jitter in these future detector systems such per-cell timestamps granularity and per-cell configuration parameters, providing a highly flexible signal processing environment. However, processing hundreds of timestamps per detection event places a toll on the real-time processing, which increases rapidly with embedded channel count. Furthermore, if the processing is sent to an external device such as an FPGA, the bandwidth and related power requirements also increase. The simulation flow presented here offers perspectives on how many time to digital converters (TDC) would be required to reach the 10 ps FWHM CTR range for PET. Using this information, designers can estimate the compromises between timing performance, bandwidth requirements, data transmission, power consumption and real-time dataflow processing in the DAQ at the chip and system level. With a standard 1.1 × 1.1 × 3.0 mm3LYSO scintillator, the coincidence timing resolution (CTR) changed by less than 3% within the range of 4 to 484 implemented TDCs for evaluated system conditions. On the other hand, an LYSO-based photonic crystal with 2.5% prompt emission rate needs a detector with at least 36 TDCs to reach within 3% CTR of an equivalent array with one TDC per SPAD. This gives significant insights on how this change of crystal material will affect system real time requirements for future detector design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".