MétaCan
Menu
Back to cohort

Design and Evaluation of a Partly Segmented Light Guide for High Resolution Solid-State Positron Emission Tomography (PET) Detectors

2024· article· en· W4402833401 on OpenAlexafffund
Henry Maa-Hacquoil, Harutyun Poladyan, Brandon Baldassi, Oleksandr Bubon, A. Reznik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPositron emission tomographyDetectorSolid-statePositron emissionResolution (logic)TomographyMedical physicsPositronNuclear medicinePhysicsMaterials scienceOpticsNuclear physicsComputer scienceArtificial intelligenceMedicineEngineering physicsElectron

Abstract

fetched live from OpenAlex

The Radialis organ-targeted PET camera utilizes planar detector technology based on tileable individual detector blocks. Each detector block consists of a 24×24 array of polished LYSO crystals with a pitch size of 2.4 mm, coupled to an 8×8 array of silicon photomultipliers (SiPMs). A borosilicate light guide is positioned between the scintillation crystal array and the photosensors for light sharing and multiplexed readout. However, LYSO crystal identification degrades at the block edges due to asymmetrical light sharing and reflection. To address this, we developed a partly segmented light guide with slits filled with barium sulfate to redistribute light spread near the detector edges. Block detector prototypes with partially segmented light guides were evaluated using a 4-channel readout and a 64-channel PETsys readout, demonstrating improved coordinate reconstruction and edge LYSO crystal identification, especially with 4.0 mm slits and Enhanced Specular Reflector (ESR) wrapping.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.019
GPT teacher head0.305
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicDigital Radiography and Breast ImagingFrench-language works237,207