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Record W4310502358 · doi:10.1109/ius54386.2022.9958511

Multifrequency Liver Shear Wave Absolute Vibro-Elastography with an xMATRIX Array - 2D vs. 3D Comparison Study

2022· article· en· W4310502358 on OpenAlexafffund
Qi Zeng, Shahed K. Mohammed, Tajwar Abrar Aleef, Emily Pang, Jin Ho Chang, James Jago, Robert Rohling, Septimiu E. Salcudean

Bibliographic record

Venue2022 IEEE International Ultrasonics Symposium (IUS) · 2022
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsVancouver General HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic resonance elastographyElastographyImaging phantomPhasorShear (geology)Magnetic resonance imagingBiomedical engineeringAcousticsIterative reconstructionPhysicsMaterials scienceMedicineUltrasoundRadiologyOptics

Abstract

fetched live from OpenAlex

Shear wave absolute vibro-elastography (S-WAVE) is a quantitative technique that can provide volumetric hepatic stiffness measurements to aid the diagnosis and monitoring of chronic liver diseases. In our previous studies, we have shown that xMATRIX based 3D S-WAVE imaging system can generate large field of view shear wave phasors, and the volumetric reconstruction results are comparable to Magnetic Resonance Elastography (MRE). In recent literature, 2D vibro-elastography techniques are still the current state-of-the-art, and no study has compared inherent performance differences between the 2D and 3D approaches in a controlled setting. In this study, we address this gap in the literature. With an updated S-WAVE imaging setup, a set of four liver tissue phantoms and five healthy subjects were imaged. 3D MRE was used as the reference method for a cross-comparison study. Our results show that the 2D reconstruction with S-WAVE phasor data showed a consistent overestimation pattern in both the phantom and in vivo dataset, resulting in a lower agreement to the MRE results when compared to the 3D reconstruction in a similar setting.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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 designObservational
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

Citations5
Published2022
Admission routes2
Has abstractyes

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