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Record W2972296307 · doi:10.1109/nssmic.2018.8824315

Improved coordinate reconstruction for SiPM based gamma detector with pixelated scintillating crystal and multiplexed readout

2018· article· en· W2972296307 on OpenAlexaff
Harutyun Poladyan, Oleksandr Bubon, Sergii Senchurov, A. Teymurazyan, A. Reznik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversity of ReginaLakehead University
Fundersnot available
KeywordsSilicon photomultiplierLyso-PhysicsDetectorScintillationMultiplexingOpticsPhotomultiplierSIGNAL (programming language)Nuclear electronicsOptoelectronicsScintillatorElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Currently the prevailing high-resolution Positron Emission Tomography (PET) detector architecture consist of a high-density and high-atomic number scintillating crystals coupled to a pixelated photosensitive layer such as position sensitive photomultiplier tubes (PS-PMTs) or silicon photomultiplier (SiPM) arrays. Such detectors typically rely on some form of analog signal multiplexing in order to minimize the number of readout electronics channels and the positions of the gamma ray interactions are calculated using Center-of-Gravity (CoG) method.In this paper we report on the Truncated Center of Gravity (TCoG) and Raised To the Power (RTP) methods for reconstruction of the positions of the gamma ray interactions as applied to a newly developed small-scale PET detector prototype based on 24x24 LYSO scintillation crystal arrays and 8x8 SiPM arrays. In the current small-scale prototype, the analog signal readout is optimized with 64:16 multiplexing ratio.A notable advantage of using TCoG or RTP algorithms instead of basic Centre-of-Gravity (CoG) is demonstrated. Particularly, the Field of View (FOV) distortion inherent to CoG reconstruction is eliminated.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.511

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 head0.009
GPT teacher head0.223
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations3
Published2018
Admission routes1
Has abstractyes

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