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Record W1972405335 · doi:10.1109/isbi.2013.6556592

Adaptive compounding of synthetic aperture and compounded plane-wave imaging for fast ultrasonography

2013· article· en· W1972405335 on OpenAlexaff
Kyle Kotowick, Robert Rohling, Lutz Lampe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBeamformingFrame rateSignal-to-noise ratio (imaging)Aperture (computer memory)Imaging phantomCompoundingComputer scienceAcousticsNoise (video)SIGNAL (programming language)Focus (optics)OpticsPhysicsMaterials scienceArtificial intelligenceTelecommunicationsImage (mathematics)

Abstract

fetched live from OpenAlex

This paper describes the investigation and implementation of a combined synthetic aperture (SA) and compounded plane wave (CPW) ultrasound imaging technique. Individually, these methods offer unique benefits over the conventional transmit beamforming used in ultrasonography (higher resolution and increased signal-to-noise ratio, respectively), and both have the potential for a higher physical frame rate limit. The technique described combines the individual benefits while maintaining a higher frame rate, by adaptively compounding the resulting images from SA and CPW sequences at half the standard number of transmits. Tests on a phantom with both point scatterers and occlusions showed overall improvements of 14.5% in lateral resolution and 7.5% in axial resolution over CPW, and a 14.5 dB improvement in signal-to-noise ratio (SNR) for occlusions over SA. Both resolution and SNR were an improvement over that for conventional transmit beamforming with two focus depths, and offer double the physical frame rate limit.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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