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Record W2277068097 · doi:10.1117/1.jmi.3.1.016001

Fabrication and control of CT number through polymeric composites based on coronary plaque CT phantom applications

2016· article· en· W2277068097 on OpenAlexafffund
Carlton F. O. Hoy, Hani E. Naguib, Narinder Paul

Bibliographic record

VenueJournal of Medical Imaging · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMitacsUniversity Health Network
KeywordsImaging phantomFabricationHounsfield scaleBiomedical engineeringMedicineThermoplastic polyurethanePolyvinylidene fluorideMaterials scienceComposite materialPolymerComputed tomographyNuclear medicineRadiologyElastomer

Abstract

fetched live from OpenAlex

Biomedical phantoms are commonly used for various medical imaging modalities to improve imaging quality and procedures. Current biomedical phantoms fabricated commercially are high in cost and limited in the specificity of human environments and structures that can be mimicked. This study aimed to control the measurable computed tomography (CT) number in Hounsfield units through polymeric biomedical phantom materials using controlled amounts of hydroxyapatite (hA). The purpose was to fabricate CT phantoms capable of mimicking various coronary plaque types while introducing a fabrication technique and basis for a numerical model to which the technique may be applied. The CT number is tunable based on the controlled material properties of electron density and atomic numbers. Three different polymeric matrices of polyethylene (PE), thermoplastic polyurethane (TPU), and polyvinylidene fluoride (PVDF) were selected due to their varied specific densities and ease of fabrication acting as integral properties for CT phantom fabrication. These polymers were processed together with additions of hA in mass percentages of 2.5, 5, 10, and 20% hA as well as a 0% hA as a control for each polymeric material. By adding hA to PE, TPU, and PVDF an increasing trend was exhibited between CT number and weight percent of hA.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.323

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 head0.005
GPT teacher head0.256
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations8
Published2016
Admission routes2
Has abstractyes

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