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Record W2151317530 · doi:10.2967/jnmt.107.040782

Quantitative PET Comparing Gated with Nongated Acquisitions Using a NEMA Phantom with Respiratory-Simulated Motion

2007· article· en· W2151317530 on OpenAlexaff
Douglass Vines, H. Keller, Jeremy Hoisak, Stephen Breen

Bibliographic record

VenueJournal of Nuclear Medicine Technology · 2007
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsImaging phantomNuclear medicinePhysicsVoxelSPHERESDisplacement (psychology)Partial volumeMaterials scienceBiomedical engineeringOpticsMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

UNLABELLED: This study evaluated the use of gated versus nongated PET acquisitions for absolute quantification of radioisotope concentration (RC) in a respiratory motion-simulated moving phantom filled with radioactive spheres and background for both 2-dimensional (2D) and 3-dimensional (3D) acquisitions. METHODS: An image-quality phantom with all 6 spheres filled with the same (18)F RC (range, 19-62 kBq/mL) was scanned with PET/CT at rest and in motion with and without gating. The background was filled with (18)F solution to yield sphere-to-background ratios of approximately 5, 10, 15, and 20 to 1. Both 2D and 3D acquisitions were used for all combinations. Respiratory motion was simulated by using a motor-driven plastic platform to move the phantom periodically with a displacement of 2 cm and a cycle time of 5.8 s. For gated acquisitions, the phantom was tracked using a real-time position management system. Images were reconstructed, and regions of interest with the same sizes as the actual spheres were manually placed on axial slices to determine maximum and mean pixel RC. A threshold method (70% and 94% for 2D and 3D modes) was also used to determine a mean voxel RC. All values were compared with the expected RC; percentage differences were calculated for each sphere. To reduce partial-volume effects, only data for the 4 largest spheres were analyzed. RESULTS: The mean pixel method was the only method with linear responses for all 3 scan types, enabling direct comparisons. The ranges of RC percentage differences were underestimated for all scan types (using the mean pixel method). The overall mean percentage differences were 37, 49, and 41 in 2D mode and 40, 51, and 41 in 3D mode for static, nongated, and gated acquisitions, respectively. Gated acquisitions improved quantification (by reducing underestimation) over nongated acquisitions by 8% and 10% for 2D and 3D modes. CONCLUSION: In the presence of motion, the use of gated PET acquisitions appears to improve quantification accuracy over nongated acquisitions, almost restoring the results to those observed when the phantom is static.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.053
GPT teacher head0.370
Teacher spread0.317 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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