MétaCan
Menu
Back to cohort
Record W2086118747 · doi:10.1088/0031-9155/49/24/009

Poly(vinyl alcohol) cryogel phantoms for use in ultrasound and MR imaging

2004· article· en· W2086118747 on OpenAlexaff
Kathleen Surry, Hillary Austin, Aaron Fenster, Terry M. Peters

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsVinyl alcoholUltrasoundBiomedical engineeringMaterials scienceNuclear medicineAlcoholRadiologyMedicineChemistryComposite materialPolymer

Abstract

fetched live from OpenAlex

Poly(vinyl alcohol) cryogel, PVA-C, is presented as a tissue-mimicking material, suitable for application in magnetic resonance (MR) imaging and ultrasound imaging. A 10% by weight poly(vinyl alcohol) in water solution was used to form PVA-C, which is solidified through a freeze-thaw process. The number of freeze-thaw cycles affects the properties of the material. The ultrasound and MR imaging characteristics were investigated using cylindrical samples of PVA-C. The speed of sound was found to range from 1520 to 1540 m s(-1), and the attenuation coefficients were in the range of 0.075-0.28 dB (cm MHz)(-1). T1 and T2 relaxation values were found to be 718-1034 ms and 108-175 ms, respectively. We also present applications of this material in an anthropomorphic brain phantom, a multi-volume stenosed vessel phantom and breast biopsy phantoms. Some suggestions are made for how best to handle this material in the phantom design and development process.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.189
GPT teacher head0.443
Teacher spread0.254 · 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

Citations355
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207