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Record W2162494140 · doi:10.1109/ultsym.2007.225

10B-6 A Composite Imaging Technique for High Frame-Rate and Full-View Cardiovascular Ultrasound and Elasticity Imaging

2007· article· en· W2162494140 on OpenAlexaboutno aff
S. Wang, Wei-Ning Lee, Jianwen Luo, Elisa E. Konofagou

Bibliographic record

VenueProceedings/Proceedings - IEEE Ultrasonics Symposium · 2007
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsnot available
Fundersnot available
KeywordsUltrasound imagingUltrasonic imagingElasticity (physics)UltrasoundMedical imagingComputer scienceBiomedical engineeringMaterials scienceRadiologyMedicineArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

The frame-rate of ultrasound radio-frequency (RF) data acquisition is critical for imaging of the pulse wave and electromechanical wave propagation in cardiovascular tissues as well as improving the strain estimation. Therefore, an automated method had been developed to overcome the frame-rate limitations on standard systems by retrospective multi-sector signal acquisition through an electrocardiogram (ECG) gating technique. The method achieved a frame rate of 481 Hz at a 100% field of view, 64 line densities and an imaging depth of 11 cm. The composite full-view images were reconstructed by retrospectively combining seven small-sector RF frames using the ECG-gating technique. The axial displacements of both long-axis and short-axis views of a human left ventricle and a long-axis view of the abdominal aorta were calculated using an RF based speckle-tracking technique comprising ID cross-correlation methods in a 2D search (window size of 6.9 mm and overlap of 80%). Several sequences of electromechanical waves propagating in a left ventricular long-axis and short-axis view, and long-axis view of abdominal aorta were imaged at high frame rates. Currently, the method was implemented on an Ultrasonix RP system (Ultrasonix Medical Corp. Richmond, Canada) and could be potentially implemented on other clinical systems.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.186
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

Explore more

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