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Record W4212827646 · doi:10.3389/fphy.2022.791145

Ultrasound Contrast Imaging: Fundamentals and Emerging Technology

2022· article· en· W4212827646 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

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.475

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 head0.004
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