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Assessing the Performance of the TOFPET2c ASIC at X-ray Energies for Time-of-Flight Scatter Rejection Applications

2024· article· en· W4402834047 on OpenAlexaff
L.-D. Gaulin, Vanessa Nadig, Julien Rossignol, F. Gagnon, Katrin Herweg, Volkmar Schulz, S. Gundacker, Réjean Fontaine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsApplication-specific integrated circuitTime of flightComputer sciencePhysicsAerospace engineeringOpticsEngineeringEmbedded system

Abstract

fetched live from OpenAlex

The addition of a time-of-flight (ToF) measurements to radiography and computed tomography (CT) opens the door to an anti-scatter grid-free approach to scatter rejection in imaging systems, potentially increasing system sensitivity and image quality. Previously developed hardware limited to a few channels showed that the ToF scatter rejection is possible, but lacked in scale and density. A medium-scale ToF scatter rejection detection module was developed, allowing for the evaluation of off-the-shelf ToF ASICs, as well as the future development of dedicated devices. The TOFPET2c ASIC designed by PETSys showed good potential for the first detector, offering high integration and pixel count. The evaluation of the TOFPET2c ASIC at X-ray energies (20 keV to 140 keV) showed a 414 ps FWHM detector timing resolution. Such a value is slightly over the 300 ps FWHM limit sufficient to reach scatter-rejection similar to anti-scatter grids and requires further investigation. An energy resolution bellow 50% was also reached for all desired energies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.262
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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