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Record W4394955677 · doi:10.1016/j.ejmp.2024.103357

Development of novel low-cost readout electronics for large field-of-view gamma camera detectors

2024· article· en· W4394955677 on OpenAlexaff
Aram Radnia, Amirhossein Alikhani, Behnoosh Teimourian, Mahyar Youser Nejad, Mohammad Hossein Davood Abadi Farahani, Fakhereh Pashaei, Arman Rahmim, Habib Zaidi, Mohammad Reza Ay

Bibliographic record

VenuePhysica Medica · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersTehran University of Medical Sciences and Health ServicesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDetectorGamma cameraElectronicsField (mathematics)PhysicsComputer scienceElectrical engineeringOpticsEngineeringMathematics

Abstract

fetched live from OpenAlex

PURPOSE: Large scintillation crystals-based gamma cameras play a crucial role in nuclear medicine imaging. In this study, a large field-of-view (FOV) gamma detector consisting of 48 square PMTs developed using a new readout electronics, reducing 48 (6 × 8) analog signals to 14 (6 + 8) analog sums of each row and column, with reduced complexity and cost while preserving image quality. METHODS: All 14 analog signals were converted to digital signals using AD9257 high-speed analog to digital (ADC) converters driven by the SPARTAN-6 family of field-programmable gate arrays (FPGA) in order to calculate the signal integrals. The positioning algorithm was based on the digital correlated signal enhancement (CSE) algorithm implemented in the acquisition software. The performance characteristics of the developed gamma camera were measured using the NEMA NU 1-2018 standards. RESULTS: The measured energy resolution of the developed detector was 8.7 % at 140 keV, with an intrinsic spatial resolution of 3.9 mm. The uniformity was within 0.6 %, while the linearity was within 0.1 %. CONCLUSION: The performance evaluation demonstrated that the developed detector has suitable specifications for high-end nuclear medicine imaging.

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 score0.921
Threshold uncertainty score0.420

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 head0.016
GPT teacher head0.292
Teacher spread0.276 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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