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Record W2054276653 · doi:10.1088/0031-9155/49/10/016

Performance evaluation of the 16-module quad-HIDAC small animal PET camera

2004· article· en· W2054276653 on OpenAlexfundno aff
J. Missimer, Zoltan L Madi, Michael Honer, Claudia Keller, August P. Schubiger, Simon-Mensah Ametamey

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsnot available
FundersCentre for Addiction and Mental Health
KeywordsImaging phantomOpticsPhysicsDetectorGamma cameraImage resolutionField of viewLine sourceNuclear medicineMaterials scienceMedicine

Abstract

fetched live from OpenAlex

The quad-HIDAC small animal PET camera is a quadratic array of high-density avalanche chambers; the camera described in this publication consists of 16 modules. We present the system response using point and line sources and a mouse phantom. The quad-HIDAC camera exhibits a count rate stability of better than 1% and linearity of response to coincidences up to 2.2 x 10(5) cps at 16 MBq activity. Corrected for deadtime and random coincidences, the efficiency for the line source is 0.011, of which unscattered coincidences yield 0.009. The scatter fraction originating from the detectors is 0.22. Absorption within the mouse phantom was 20% and the scatter fraction increased to 0.29. Resolution is uniform within the entire field-of-view, which is 28 cm axially and 17 cm radially. Reconstruction of a point source yields a resolution of 1.1 mm FWHM for all three components. The performance of the camera demonstrates its excellent suitability for the functional imaging of small animals.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.221
GPT teacher head0.430
Teacher spread0.209 · 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 designBench or experimental
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

Citations72
Published2004
Admission routes1
Has abstractyes

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