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Record W2117960258 · doi:10.1109/nssmic.1995.510450

Imaging performance of PEM-1: a high resolution system for positron emission mammography

2002· article· en· W2117960258 on OpenAlexafffund
Christopher J. Thompson, K. Murthy, R.L. Clancy, James L. Robar, Alanah Bergman, Robert Lisbona, Antoine Loutfi, J. Gagnon, I. Weinberg, R. Mako

Bibliographic record

Venue1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record · 2002
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Cancer InstituteMedical Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsMammographyPositron emission tomographyScannerPhotomultiplierNuclear medicinePositronImage resolutionPositron emissionPhysicsCoincidenceFull width at half maximumBreast imagingOpticsDetectorMedicineBreast cancerNuclear physicsPathology

Abstract

fetched live from OpenAlex

Positron emission tomography with /sup 18/F-fluoro-deoxyglucose (FDG) is known to detect the increased metabolism of breast tumours. The authors have built a positron emission mammography system which produces metabolic breast images co-registered with conventional mammography. Two position sensitive photomultipliers are coupled to four BGO blocks which are partially cut into 2/spl times/2 mm elements from both faces. This provides 72/spl times/72 BGO crystals in coincidence yielding over 26 million lines of response. A real time display is formed by performing a weighted back-projection onto seven image planes through the breast. The prototype instrument is currently being tested on phantoms. The spatial resolution in the central region has been measured at 2.05 mm FWHM, less than half that of a conventional PET scanner. The coincidence resolving time is 12 nsec. This device should be able to identify breast tumours <5 mm in diameter and provide 3-D localization registered with conventional mammography.

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)0.000
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 score0.977
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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

Citations18
Published2002
Admission routes2
Has abstractyes

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