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Record W2042652492 · doi:10.1109/nssmic.2006.353715

Simultaneous Attenuation and Scatter Corrections in Small Animal PET Imaging

2006· article· en· W2042652492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venue2006 IEEE Nuclear Science Symposium Conference Record · 2006
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAttenuationCorrection for attenuationCompton scatteringPhotonPhysicsOpticsVoxelScatteringPositron emission tomographyScannerComputational physicsNuclear medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this work is to simultaneously correct for attenuation and scatter in positron emission tomography (PET) by analytically assessing the distribution of the scattered photons using the emission images, the probability of scatter interactions and the detection efficiency. Above the usual lower energy threshold of 300 keV, the attenuated photons are dominantly those which have undergone a Compton scattering. A simple equation is established by considering that each voxel in the image is the measurement of the transmitted photons through the subject, added to the contribution from the other sources by means of their scatter. The solution of this equation allows to correct from scatter and attenuation simultaneously. This new method was applied for data measured with the Sherbrooke small animal PET scanner in line sources, hot spot phantoms, and in rat hearts and tumors.

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.

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 score0.959
Threshold uncertainty score0.586

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 studies0.0000.001
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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