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Record W4212827646 · doi:10.3389/fphy.2022.791145

Ultrasound Contrast Imaging: Fundamentals and Emerging Technology

2022· article· en· W4212827646 on OpenAlexafffund
Hossein Yusefi, Brandon Helfield

Bibliographic record

VenueFrontiers in Physics · 2022
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsConcordia University
FundersFonds de recherche du QuébecNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsHeart and Stroke Foundation of CanadaBurroughs Wellcome Fund
KeywordsUltrasoundModality (human–computer interaction)Contrast-enhanced ultrasoundScope (computer science)Diagnostic ultrasoundMedical imagingMedical physicsMicrobubblesMedicineUltrasound imagingMedical ultrasoundContrast (vision)RadiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The development of microbubble contrast agents has broadened the scope of medical ultrasound imaging. Along with dedicated imaging techniques, these agents provide enhanced echoes from the blood pool and have enabled diagnostic ultrasound to assess and quantify microvascular blood flow. Contrast-enhanced ultrasound is currently used worldwide with clinical indications in cardiology and radiology, and it continues to evolve and develop through innovative technological advancements. In this review article, we present an overview of the basic microbubble physics and bubble-specific imaging techniques that enable this modality, and follow this with a discussion on new and emerging applications.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations82
Published2022
Admission routes2
Has abstractyes

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