High Rate Photon Counting CT Using Parallel Digital PET Electronics
Bibliographic record
Abstract
Recent developments in detectors and electronics enable both positron emission tomography (PET) and X-ray computed tomography (CT) data to be acquired concurrently using the same detection front-end for dual-modality PET/CT imaging. Moreover, it would potentially allow substantial reduction of cost and housing size, in addition to facilitating image fusion. However, the lower energy signals ( ~60 keV versus 511 keV) and higher photon flux per pixel ( > 1 Mcps versus 10 kcps) in CT relative to PET cause significant pile-up and dead-time in CT data acquired in photon counting mode. A digital signal processing method was developed and implemented to improve processing of detector signals sampled at low frequency (~ 45 MHz) in presence of pile-up. The method consists in digitally subtracting the detector impulse response at the output of the preamplifier to restore the signal baseline for more accurate energy estimation. When compared to a fixed threshold counting technique, the proposed method features better noise immunity, higher energy resolution and 50% higher rates measured at an estimated true rate of 2.75 Mcps, making CT integration within modern digital PET hardware feasible.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".