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Record W2537863741 · doi:10.1109/nssmic.2010.5874161

Analytic pulse height correction in dual-ended readout PET detectors

2010· article· en· W2537863741 on OpenAlexaff
Farhad Taghibakhsh, Craig S. Levin, J. A. Rowlands

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsThunder Bay Regional Research InstituteUniversity of Toronto
Fundersnot available
KeywordsLyso-DetectorCalibrationAsymmetryEnergy (signal processing)SIGNAL (programming language)PhysicsOpticsResolution (logic)Pulse (music)Computational physicsComputer scienceArtificial intelligenceParticle physics

Abstract

fetched live from OpenAlex

We propose an analytical method for pulse height correction to enable a global energy spectrum and to restore uniformity of signal asymmetry used for extraction of depth of interaction (DOI) information in PET detectors with dual-ended readout configuration. The method is based on empirically modifying the pulse heights obtained from two ends of crystals to correct for non-linear dependency of light output on DOI before calculating energy or DOI. Experiments with saw-cut, high aspect ratio crystals (1×1×20 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> LYSO) showed that an improvement in global energy resolution from 48% to 18% is obtained using our method without any calibration of the energy spectrum based on DOI. We also observed improvement in uniformity of signal asymmetry along the crystal by correcting pulse heights using the proposed method. Details of correction method, its effect on energy resolution and signal asymmetry and/or DOI profile, as well as results of optimization of the method based on maximizing the peak-to-valley ratio of the photopeak are presented and discussed. The proposed method finds application in calibration and signal processing of DOI PET detectors based on dual-ended readout configuration.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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.

Study designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

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