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Record W2116016066 · doi:10.3233/bme-140980

Low-cost quasi-real-time elastography using B-mode ultrasound images

2014· article· en· W2116016066 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

VenueBio-Medical Materials and Engineering · 2014
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of Waterloo
FundersDivision of Integrative Organismal SystemsNatural Sciences and Engineering Research Council of Canada
KeywordsElastographyUltrasound elastographyUltrasoundImaging phantomScannerComputer scienceBiomedical engineeringArtificial intelligenceMedicineRadiology

Abstract

fetched live from OpenAlex

A low cost, quasi real-time elastography system, displacement-gradient elastography (DGE), was developed by applying digital image correlation (DIC) method and smoothing algorithm to B-mode ultrasound images. In order to achieve quasi real-time elastogram display, a new fast pattern matching algorithm, decoupled cross-correlation (DCC), was proposed and validated. By applying the DGE to various phantoms, elastograms were generated to identify the lesion with wide variations of stiffness ratio and applied strain. The performance of DGE was qualitatively compared with those from a high-end ultrasound scanner using the elastograms of a commercial elastography breast phantom. DGE was also applied to the ultrasound images of human breast lesions in various BI-RADS categories. This study suggests that DGE may have comparable performance to conventional elastography in detecting breast cancer, while it can be easily implemented onto conventional ultrasound scanners.

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 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 score0.371
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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