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Timing resolution of a TOF-DOI detector module prototype for positron emission tomography

2025· article· en· W4413076850 on OpenAlexaff
Vanessa Nadig, Giulia Terragni, Ekaterini Toumparidou, Matthias Hornisch, Artur Selenski, Katrin Herweg, E. Tribbia, E. Auffray, Volkmar Schulz, S. Gundacker

Bibliographic record

VenueJournal of Instrumentation · 2025
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsHyperion Technologies (Canada)
Fundersnot available
KeywordsDetectorFull width at half maximumOpticsImage resolutionPhysicsResolution (logic)Image qualitySilicon photomultiplierField of viewParallaxScintillatorComputer scienceComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract To increase the image quality and spatial resolution in positron emission tomography (PET), organ-dedicated and large axial field of view scanners require precise correction of the parallax errors at oblique angles via depth-of-interaction methods. In addition to their large solid angle coverage, time-of-flight (TOF) information, i.e., a high coincidence time resolution (CTR), can be exploited to increase the image signal-to-noise ratio and effectively boost system sensitivity. Due to the impact of the photon travel time spread (PTS) on the TOF resolution, a high DOI resolution is essential to correct the impact of the PTS on the CTR and achieve even higher timing resolution. In this work, we present a TOF- and DOI-capable detector block with a CTR of 187 ps (FWHM) to 213 ps (FWHM), read out with the NINO and TOFPET2 ASIC, and a DOI resolution of 3.4 mm to 4.7 mm (RMSE), which distinguishes at least two DOI layers in the detector. Additionally, we established benchmarks for the CTR with custom high-frequency readout electronics. With these, a CTR of 136 ps (FWHM) can be maintained for a single TOF-DOI unit channel in the detector block.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.172

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 head0.022
GPT teacher head0.349
Teacher spread0.328 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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