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Record W2795111039 · doi:10.1002/mp.12893

Sheet beam x‐ray fluorescence computed tomography (XFCT) imaging of gold nanoparticles

2018· article· en· W2795111039 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsUniversity of Victoria
FundersBC Cancer AgencyNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComputed tomographyMedical imagingColloidal goldX-rayNanoparticleOpticsTomographyMedical physicsFluorescenceComputed tomography laser mammographyNuclear medicineNuclear magnetic resonanceRadiologyMedicinePreclinical imagingNanotechnologyPhysics

Abstract

fetched live from OpenAlex

PURPOSE: X-ray fluorescence computed tomography (XFCT) experiments have typically used pencil beams for data acquisition, which yielded good quality images of gold nanoparticles (AuNP) but prolonged the imaging time. Here we propose three novel collimator geometries for use with faster sheet beam XFCT data acquisition. The feasibility of a multipinhole, parallel, and converging collimator was investigated in a Monte Carlo study. METHODS: A cylindrical water phantom with 2 cm in diameter and 3 cm in height containing 0.5-2 mm diameter vials with 0.4%-1.6% AuNP concentrations was modelled by FLUKA. A 15 and 81 keV monoenergetic x-ray sheet beam of 0.4 mm in width was used to image the phantom with L-shell and K-shell XFCT, respectively, with a dose of 30 mGy. The collimator thickness for L-shell and K-shell data acquisition was 3.3 and 5.1 mm, respectively. The XFCT images resulting from three collimator geometries were generated using the maximum likelihood expectation maximization (MLEM) iterative reconstruction method. With a resolution of 0.4 mm they were corrected for x-ray attenuation. The sheet beam XFCT images were compared against pencil beam geometry images that were generated using 55 translations. To assess image quality, the contrast-to-noise ratio (CNR) was evaluated for each vial. The Rose criterion was used to determine the lowest AuNP concentration detectable for each image. RESULTS: Among the three collimator geometry types, the sheet beam L-shell and K-shell parallel collimator XFCT images yielded AuNP sensitivity limits at 0.09% and 0.08%, respectively, for a 2 mm diameter vial. The AuNP sensitivity limits of the pencil beam XFCT images were 0.07% and 0.01% for L-shell and K-shell XFCT, respectively. The L-shell parallel collimator AuNP imaging sensitivity approached that of the pencil beam geometry with a 55-fold reduction in imaging time. The AuNP sensitivity limits for the 1 mm diameter vial for the L-shell and K-shell parallel collimator XFCT images were 0.19% and 0.16%, respectively, and those of the pencil beam XFCT images were 0.08% and 0.01% for L-shell and K-shell XFCT, respectively. The remaining two collimator geometries resulted in a lower CNR and poorer image quality. For a 2 mm diameter vial, the AuNP sensitivity limits for the L-shell and K-shell multipinhole collimator XFCT images were 0.23% and 0.52%, respectively, while for the L-shell and K-shell converging collimator XFCT images the AuNP sensitivity limits were 0.38% and 0.13%, respectively. CONCLUSION: This work demonstrates the feasibility of sheet beam L-shell XFCT imaging for small animal studies using parallel-oriented lead collimators which can detect AuNP concentrations approaching the level of pencil beam images with reduced imaging time.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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