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Record W2898776965 · doi:10.1088/1361-6560/aaece9

Optimization of a table-top x-ray fluorescence computed tomography (XFCT) system

2018· article· en· W2898776965 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

VenuePhysics in Medicine and Biology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute Canada
KeywordsSpectrometerImaging phantomOpticsMaterials scienceMonte Carlo methodDetectorPhysics

Abstract

fetched live from OpenAlex

Abstract Pencil beam x-ray fluorescence computed tomography (XFCT) has typically used a single spectrometer and prohibitively long scan times. However, detecting backscattered fluorescent x-rays from gold nanoparticles (AuNPs) using multiple spectrometers greatly reduces image noise and scan time. The arrangement of eight spectrometers for combined K-shell and L-shell XFCT was investigated along with a variety of conditions. A 2.5 cm-diameter cylindrical water phantom containing 4 mm-diameter vials with 0.1%–2% AuNP concentrations by weight was modeled by TOPAS, a GEANT4-based Monte Carlo software. The phantom was irradiated to 30 mGy by a 0.5 mm Pb-filtered 120 kVp and 1 mm Al-filtered 30 kVp 1 mm 2 x-ray pencil beam to yield respective Au K-shell and L-shell fluorescent x-rays, with 50 0.5 mm translation and 2-degree rotation steps. Eight CdTe and silicon drift detector (SDD) spectrometers were placed 2.25 cm away from the isocentre. The respective energy resolution was applied to the detected energy spectra and the spectra were corrected for detector response before extracting the fluorescence signal. Three CdTe and SDD spectrometer configurations (isotropic/backscattered grid/backscattered row arrangements), two CdTe crystal sizes (9 mm 2 /25 mm 2 ), two scanning techniques (moving/stationary spectrometers) and five vial-edge depths (0–4 mm) were considered in optimizing the contrast-to-noise ratio (CNR) for each XFCT image reconstructed with a maximum-likelihood expectation maximization (MLEM) algorithm. The isotropic spectrometer arrangement had AuNP detection sensitivities of 0.106% for K-shell and 0.132% for L-shell XFCT at 4 mm depth. Comparatively, the backscattered grid arrangement had the best AuNP sensitivity of 0.055% and 0.095%. The highest K-shell (0.044%) and L-shell (0.004%) AuNP sensitivities were found for vials at 0 mm depth. Using stationary spectrometers or the 9 mm 2 CdTe crystal compromised the CNR. For the best-case arrangement, L-shell XFCT is superior at vial-edge depths less than 3.0 mm. This work demonstrated the importance of spectrometer arrangement and vial depth for improving AuNP sensitivity and will guide the design for our table-top XFCT system.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.276

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 studies0.0000.000
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 head0.043
GPT teacher head0.295
Teacher spread0.251 · 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