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Record W2903964823 · doi:10.1109/trpms.2018.2885439

Performance Characterization of MPPC Modules for TOF-PET Applications

2018· article· en· W2903964823 on OpenAlexafffund
Andrew L. Goertzen, Devin Van Elburg

Bibliographic record

VenueIEEE Transactions on Radiation and Plasma Medical Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of ManitobaResearch Manitoba
KeywordsFull width at half maximumDetectorPhysicsLyso-OpticsEnergy (signal processing)Scintillator

Abstract

fetched live from OpenAlex

PET detectors commonly feature large numbers of multipixel photon counters (MPPCs), requiring data acquisition systems with significant signal multiplexing or using application specific integrated circuits (ASICs) for independent MPPC readout. We evaluate here two Hamamatsu C13500-4075LC-12 detector modules designed for time-of-flight (TOF) PET. Each module has a 12 × 12 lutetium fine silicate crystal array (4.2 χ 4.2 × 20 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) one-to-one coupled to a 12 × 12 array of 4 mm MPPCs. Each detector has 8 18-channel ASICs utilizing time-over-threshold (ToT) readout. At low event rates, the detectors have full-width at half-maximum (FWHM) energy resolution of 11.8%±0.4%, measured in linearized energy spectra, and 275 ps FWHM coincidence timing resolution (CTR) using energy window of ±1 photopeak FWHM. The 511 keV photopeak amplitude in the ToT spectrum changed 0.24%/°C. There was <;10% event loss up to 1450 kcps/detector. From count rates of 35-1450 kcps the 511 keV photopeak position varied by 1.2%, while photopeak FWHM in the ToT spectra increased by 54%, from 5.8% to 9.0%, corresponding to energy resolution changing from 11.8% to approximately 18.0%. Over this count rate range the CTR varied from 285 to 435 ps FWHM. The TOFPET module performance suggests they are well suited to use in whole-body PET applications.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.266

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 studies0.0000.001
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 head0.027
GPT teacher head0.309
Teacher spread0.282 · 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 designOther design
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

Citations20
Published2018
Admission routes2
Has abstractyes

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