Study of the TOFPET2c ASIC in time-of-flight detection of x-rays for scatter rejection in medical imaging applications
Bibliographic record
Abstract
Abstract Objective. Integrating time-of-flight (ToF) measurements in radiography and computed tomography (CT) enables an approach for scatter rejection in imaging systems that eliminates the need for anti-scatter grids, potentially increasing system sensitivity and image quality. However, present hardware dedicated to the time-correlated measurement of x-rays is limited to a single pixel physically too large for the desired spatial resolution. A switch to highly integrated electronics and detectors is needed to progress towards detector arrays capable of acquiring images, while offering a timing resolution below 300 ps FWHM to achieve scatter rejection comparable to current anti-scatter grids. Approach. Using off-the-shelf scintillators, photodetectors and readouts designed for ToF positron emission tomography (PET) provides a preliminary evaluation of available highly integrated readout systems supporting detector arrays for ToF scatter rejection. The TOFPET2c ASIC from PETSys offers an established development platform necessary for fast and reliable results, with no known limitation regarding time-correlated detection of medical imaging x-rays (20–140 keV). Main results. Reliable photon detection down to 31 keV was achieved, reaching energy resolutions from 23% to 92% FWHM throughout the desired energy range. Optimal detector timing resolution (DTR) from 250 ps FWHM at 130 keV to 678 ps FWHM at 30 keV was reached. Strong time walk effects were observed, showing a time shift of 642 ps up to 1740 ps between events spanning the energies used in x-ray medical imaging. Significance. The TOFPET2c ASIC has shown its potential for ToF scatter rejection, but meets the time resolution requirement of 300 ps FWHM only for limited energies (110–140 keV). This significant timing degradation observed at lower energies limits the use of the TOFPET2c ASIC for ToF scatter rejection, but offers significant advancements regarding the understanding of the phenomenon arising from the time-correlated detection of medical imaging x-rays.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".