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Record W2548703547 · doi:10.1109/nssmic.1991.259190

A new stationary sampling scheme for multi-layer positron tomographs

2002· article· en· W2548703547 on OpenAlexaff
Marjolaine Héon, Christian Carrier, J. Cadorette, P. Richard, D. Rouleau, S. Rodrigue, Roger Lecomte

Bibliographic record

VenueConference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference · 2002
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCoincidenceDetectorOffset (computer science)Sampling (signal processing)Positron emission tomographyPhysicsResolution (logic)PositronOpticsImage resolutionIterative reconstructionTomographyComputer scienceAlgorithmComputer visionNuclear medicineArtificial intelligenceNuclear physics

Abstract

fetched live from OpenAlex

A stationary sampling scheme applicable to tomographic instruments incorporating two or more detector layers is described. In this concept, the detectors in adjacent layers are angularly offset by half the interdetector distance. By reconstructing in one single slice all lines of response defined by two adjacent rings of detectors, a fourfold increase in the number of coincidence lines is obtained and a uniform sampling distance equal to one quarter the interdetector spacing is achieved. Whereas this is obtained at the expense of a 100% degradation of the resolution in the axial direction, with the recent breed of PET (positron emission tomography) scanners using nearly square cross section detectors the resolution loss is tolerable; in addition, normal reconstruction of the individual coincidence planes is always possible. The new sampling concept was investigated experimentally with a PET camera simulator. Results obtained by simulating the proposed scheme confirmed the improvement in transaxial imaging performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.001
metaresearch head score (Gemma)0.001
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 score0.961
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.073
GPT teacher head0.342
Teacher spread0.268 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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