MétaCan
Menu
Back to cohort
Record W2161440571 · doi:10.1109/nssmic.1997.670643

Postinjection attenuation correction using singles transmission on a positron tomograph without interplane septa

2002· article· en· W2161440571 on OpenAlexaff
Robert A. deKemp, Rob Beanlands

Bibliographic record

Venue1997 IEEE Nuclear Science Symposium Conference Record · 2002
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAttenuationCorrection for attenuationPhysicsImaging phantomPositron emission tomographyBackground subtractionPositron emissionTransmission (telecommunications)SubtractionOpticsNoise (video)Nuclear medicineTomographyComputer scienceMathematicsPixelMedicineTelecommunications

Abstract

fetched live from OpenAlex

Singles transmission is ideally suited for attenuation correction on PET scanners without interplane septa, producing low dead time and no randoms. However, sinogram windowing can not be used in singles mode to reject scatter or the emission background activity which is present after isotope injection. The authors propose that a simple subtraction of the singles emission background from the singles transmission scan will produce an accurate attenuation correction. The emission background is measured by performing an additional singles mode scan, but without actually exposing the /sup 137/Cs point source. Phantom studies with uniform cylinders show that the measured attenuation coefficients are restored to the true values after the emission background is subtracted. The effect on noise equivalent count rate is analogous to randoms subtraction because the singles emission background is relatively uniform throughout the field of view. The time required to perform a postinjection transmission scan is increased over a preinjection scan because the singles emission background must be measured separately.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.302
Teacher spread0.262 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

Explore more

Same venue1997 IEEE Nuclear Science Symposium Conference RecordSame topicMedical Imaging Techniques and ApplicationsFrench-language works237,207