MétaCan
Menu
Back to cohort
Record W2903162342 · doi:10.1109/mmsp.2018.8547090

Non-Local Super Resolution in Ultrasound Imaging

2018· article· en· W2903162342 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsInterpolation (computer graphics)Imaging phantomComputer scienceImage resolutionEnvelope (radar)Iterative reconstructionComputer visionSampling (signal processing)Artificial intelligenceRadio frequencyUltrasoundResolution (logic)AlgorithmImage (mathematics)OpticsPhysicsAcousticsTelecommunications

Abstract

fetched live from OpenAlex

The resolution of ultrasound (US) images is limited by physical constraints and hardware restrictions, such as the frequency, width and focal zone of the US beam. Different interpolation methods are often used to increase the sampling rate of ultrasound images. However, interpolation methods generally introduce blur in images. Herein, we present a super resolution (SR) algorithm for reconstruction of the B-mode images using the information from the envelope of radio frequency (RF) data. Our method is based on utilizing repetitive data in the nonlocal neighborhood of samples. The performance of the proposed approach is determined both qualitatively and quantitatively using phantom and in-vivo data.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Image Processing TechniquesFrench-language works237,207