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Record W2126879093 · doi:10.1117/12.844017

Simulation of low dose positron emission mammography scanner for global breast health applications

2010· article· en· W2126879093 on OpenAlexaff
William J. Ryder, I. N. Wienberg, Pavel Stepanov, A. Reznik, Maryory Urdaneta, Edward Anashkin, Michael Masoomi, Anatoly Rozenfeld

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsLakehead University
Fundersnot available
KeywordsMonte Carlo methodMammographyScannerPositron emission tomographyDetectorNuclear medicinePositronBreast cancerMedical physicsPhysicsMaterials scienceOpticsNuclear physicsMedicineCancerElectronMathematics

Abstract

fetched live from OpenAlex

Positron emission mammography ("PEM") is a breast imaging modality that typically involves the administration of relatively high doses of radiotracer. In order to reduce tracer costs and consider PEM for global screening applications, it would be helpful to reduce the required amount of administered radiotracer so that patient dose would be comparable to conventional x-ray mammograms. We performed GATE Monte Carlo investigations of several possible camera configurations. Increasing the detector thickness from 10 to 30 mm, increasing the camera surface area from 5×20cm2 to 20×20cm2, and applying depth-ofinteraction information to increase the acceptance angle, increased the overall efficiency to radiation emitted from a breast cancer by a factor of 24 as compared to existing commercial systems.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.283
Teacher spread0.274 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicMedical Imaging Techniques and ApplicationsFrench-language works237,207