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Record W4245340667 · doi:10.1109/ultsym.2017.8092657

An improved spread-spectrum method for high-frame-rate color Doppler ultrasound imaging

2017· article· en· W4245340667 on OpenAlexaff
Omar Mansour, Tamie L. Poepping, James C. Lacefield

Bibliographic record

Venue2017 IEEE International Ultrasonics Symposium (IUS) · 2017
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsWestern University
Fundersnot available
KeywordsClutterDoppler effectFrame rateOpticsComputer scienceFilter (signal processing)Plane waveSIGNAL (programming language)AcousticsPhysicsComputer visionRadarTelecommunications

Abstract

fetched live from OpenAlex

We recently introduced a spread-spectrum color Doppler imaging method that produces high-resolution, highframe-rate images without the use of compounding, thereby eliminating the tradeoffs among beam quality, frame rate, and the maximum unaliased Doppler frequency that exist for standard plane-wave imaging methods. In this manuscript, we extend the spread-spectrum method to significantly reduce tissue and wall clutter compared to our original approach. Our new method partitions the transmit sequence into multiple segments, each composed of a unique random ordering of the same set of plane-wave steering angles, so each angle repeats once per segment. The Doppler ensemble is then re-arranged into a sequence with periodically repeating angles, such that stationary clutter forms a periodic slow-time signal that is removed by applying a multi-tap comb filter. The improved clutter suppression of the new method is demonstrated using Field II simulations of imaging continuous flow through an 8 mm diameter vessel with a 5 MHz linear array.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.325
Teacher spread0.309 · 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 teacher head, not a consensus.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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