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Record W2057630337 · doi:10.1118/1.3213516

A solid iodinated phantom material for use in tomographic x‐ray imaging

2009· article· en· W2057630337 on OpenAlexafffund
Melissa L. Hill, James G. Mainprize, Gordon E. Mawdsley, Martin J. Yaffe

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImaging phantomMaterials scienceCalibrationAttenuationComputed radiographyReproducibilityRadiographyBiomedical engineeringNuclear medicineIodinated contrastProjection (relational algebra)RepeatabilityDigital radiographyMedical imagingTomographyImage qualityOpticsMedicineComputed tomographyRadiologyComputer sciencePhysicsMathematicsComputer vision

Abstract

fetched live from OpenAlex

PURPOSE: Iodinated phantoms are of value in x-ray imaging for quality control measurements, system calibration, and for use in the research setting; however, the liquid phantoms that are most often used have several limitations including variability between repeated dilutions, inhomogeneities from air bubbles or precipitants, and long set up times. Although suitable materials have been investigated for projection radiography, quantitative measurements of contrast enhancement in computed tomography (CT) have become increasingly important in the clinic, and a need exists for a durable and reproducible iodinated phantom material. In this work, the authors describe a solid radiographic phantom material that has an accurately known concentration of iodine distributed uniformly throughout its volume and that has stable properties over time. This material can be molded or machined into a desired shape to create a test object or for use in an anthropomorphic phantom. METHODS: Two sets of calibration phantoms were produced with a clinically relevant range of iodine concentrations. Measurements were made on these phantoms to characterize the material properties in terms of accuracy of iodine concentration, radiographic uniformity, temporal stability of x-ray attenuation, and manufacturing repeatability. Experimentally measured linear x-ray attenuation coefficients were compared to those predicted by a theoretical model. The uniformity of the iodine distribution in the material was assessed by measuring image intensity variation, both in projection images and in reconstructed CT volumes. The reproducibility of manufacture was estimated on a set of independently produced samples. A longitudinal study was performed to assess the stability of the material x-ray characteristics over time by making measurements at 6 month intervals. RESULTS: Good agreement was seen between the experimental measurements of effective attenuation and the theoretically predicted values. It is estimated that a desired iodine concentration could be produced to within 0.04 mg/ml. Comparison of the measured effective linear iodine attenuation coefficients of eight 1.0 mg/ml samples indicated a manufacturing reproducibility of +/-0.03 mg/ml iodine. Variations in uniformity across each of the samples were on the order of image intensity fluctuations (sigma). No inhomogeneities due to mixing or settling were apparent. An analysis of longitudinal data collected for both calibration sets revealed no perceptible change in radiographic properties over the first 6 months after manufacture, nor over a subsequent 1.5 yr period from 1 yr postmanufacture onward. CONCLUSIONS: The uniformity, stability, and precision of this iodinated material suggest that it is suitable for use in accurate calibration tools for contrast tomographic imaging.

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)0.002
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.264
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

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

Citations16
Published2009
Admission routes2
Has abstractyes

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