TDC Array Tradeoffs in Current and Upcoming Digital SiPM Detectors for Time-of-Flight PET
Bibliographic record
Abstract
Radiation detection used in positron emission tomography (PET) exploits the timing information to remove background noise and refine position measurement through time-of-flight information. Fine time resolution in the order of 10 ps full-width at half-maximum (FWHM) would not only improve contrast in the image, but would also enable direct image reconstruction without iterative or back-projected algorithms. Currently, PET experimental setups based on silicon photomultipliers (SiPMs) reach 73 ps FWHM, where the scintillation process plays the larger role in spreading the timing resolution. This will change with the optimization of faster light emission mechanisms (prompt photons), where readout optoelectronics will once more have a noticeable contribution to the timing resolution limit. In addition to reducing electronic jitter as much as possible, other aspects of the design space must also explored, especially for digital SiPMs. Unlike traditional SiPMs, digital SiPMs can integrate circuits like time-to-digital converters (TDCs) directly with individual or groups of light sensing cells. Designers should consider the number of TDCs to integrate, the area they occupy, their power consumption, their resolution, and the impact of signal processing algorithms and find a compromise with the figure of merit and the coincidence timing resolution (CTR). This paper presents a parametric simulation flow for digital SiPM microsystems that evaluates CTR based on these aspects and on the best linear unbiased estimator (BLUE) in order to guide their design for present and future PET systems. For a small $1.1\times 1.1\times3.0$ mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> LYSO crystal, the simulations indicate that for a low jitter digital SiPM microsystem with 18.2% photon detection efficiency, fewer than four timestamps with any multi-TDC configuration scheme nearly obtain the optimal CTR with BLUE (just below 100 ps FWHM), but with limited 5% improvement over only using the first observed photon. On the other hand, if a similar crystal but with 2.5% prompt photon fraction is considered, BLUE provides an improvement between 80% and 200% (depending on electronic jitter) over using only the first observed photon. In this case, a few tens of timestamps are required, yielding very different design guidelines than for standard LYSO scintillators.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".