MétaCan
Menu
Back to cohort
Record W4387257424 · doi:10.58286/28715

Interpolated Plane Wave Imaging: IPWI

2023· article· en· W4387257424 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.

Bibliographic record

Venuee-Journal of Nondestructive Testing · 2023
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsAliasingSampling (signal processing)Interpolation (computer graphics)Computer scienceNyquist frequencyImage qualityGratingTransducerNyquist–Shannon sampling theoremImage planeAlgorithmComputer visionAcousticsOpticsFilter (signal processing)PhysicsImage (mathematics)

Abstract

fetched live from OpenAlex

This paper offers a solution to the problems caused by sub-Nyquist spatial sampling in ultrasound image generation, which can result in aliasing and grating lobes in Full Matrix Capture (FMC) and Plane Wave Imaging (PWI). Neglecting the spatial sampling requirements can lead to artifacts that compromise the accuracy of ultrasound image interpretation. Although limiting the transducer element pitch to half the wavelength can alleviate grating lobes, it can also cause reduced energy transmission and limit the method's applicability in thick-walled structures. Similarly, using sparse techniques to increase acquisition speed by subsampling the firing elements is also constrained by the sampling criterion. To address these challenges, we introduce an interpolation step to PWI, which overcomes ambiguities imposed by the sampling theorem and improves image quality and acquisition speed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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