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Record W3093635528 · doi:10.1186/s42492-020-00061-x

Recent advances in applications of multimodal ultrasound-guided photoacoustic imaging technology

2020· review· en· W3093635528 on OpenAlexaff
Shanshan Wang, Zhao Yunfeng, Ye Xu

Bibliographic record

VenueVisual Computing for Industry Biomedicine and Art · 2020
Typereview
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsFujiFilm VisualSonics (Canada)
Fundersnot available
KeywordsPhotoacoustic imaging in biomedicineUltrasoundUltrasound imagingMedical imagingImaging technologyBiomedical engineeringPhotothermal therapyDoppler imagingColor dopplerMulti-mode optical fiberUltrasonic sensorComputer scienceMaterials scienceOpticsRadiologyMedicineUltrasonographyNanotechnologyOptical fiberPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Photoacoustic imaging (PAI) is often performed simultaneously with ultrasound imaging and can provide functional and cellular information regarding the tissues in the anatomical markers of the imaging. This paper describes in detail the basic principles of photoacoustic/ultrasound (PA/US) imaging and its application in recent years. It includes near-infrared-region PA, photothermal, photodynamic, and multimode imaging techniques. Particular attention is given to the relationship between PAI and ultrasonic imaging; the latest high-frequency PA/US imaging of small animals, which involves not only B-mode, but also color Doppler mode, power Doppler mode, and nonlinear imaging mode; the ultrasonic model combined with PAI, including the formation of multimodal imaging; the preclinical imaging methods; and the most effective detection methods for clinical research for the future.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.334
Teacher spread0.317 · 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

Citations35
Published2020
Admission routes1
Has abstractyes

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