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Compact and Wireless Freehand 3D Ultrasound Real-time Spine Imaging System: A pilot study

2020· article· en· W3082922232 on OpenAlexaff
Hongbo Chen, Rui Zheng, Edmond Lou, Lawrence H. Le

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of Alberta
Fundersnot available
Keywords3D ultrasoundComputer scienceComputer visionArtificial intelligenceScannerTracking (education)Data acquisitionPixelVolume (thermodynamics)Iterative reconstructionUltrasoundMedicineRadiologyPhysics

Abstract

fetched live from OpenAlex

The 3D ultrasound reconstruction technology has led to a rapid development of ultrasound spine imaging in recent decades. However, the current imaging apparatus is bulky and not portable. The objective of this study is to develop a new compact and wireless system to offer the real-time visualized spine images during data acquisition. A portable and WI-FI based ultrasound scanner and a compact EM tracking system were assembled to acquire ultrasound transverse frames with location information which could be reconstructed into 3D spine image volume in real-time. The validation was implemented on the 2D coronal images of vertebra phantoms, and the in vivo data acquisition and reconstruction were demonstrated on volunteers. The result showed that the new system could provide reconstructed spine images in real time and the average errors of the reconstructed images were about 1mm (approximate to image pixel size).

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.858

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations23
Published2020
Admission routes1
Has abstractyes

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