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Record W2894222646 · doi:10.1109/tuffc.2018.2872171

Analysis of Multifrequency and Phase Keying Strategies for Focusing Ultrasound to the Human Vertebral Canal

2018· article· en· W2894222646 on OpenAlexafffund
Stecia‐Marie Fletcher, Meaghan A. O’Reilly

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2018
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of Canada
KeywordsUltrasoundEx vivoSpinal cordBiomedical engineeringMaterials scienceMedicineIn vivoRadiologyBiology

Abstract

fetched live from OpenAlex

Focused ultrasound has been shown to increase the permeability of the blood-brain barrier and its feasibility for opening the blood-spinal cord barrier has also been demonstrated in small animal models, with great potential to impact the treatment of spinal cord (SC) disorders. For clinical translation, challenges to transvertebral focusing of ultrasound energy on the human spinal canal, such as a focal depth of field and standing-wave formation, must be addressed. A dual-aperture approach using multifrequency and phase-shift keying (PSK) strategies for achieving a controlled focus in human thoracic vertebrae was investigated through numerical simulations and benchtop experiments in ex vivo human vertebrae. An ~85% reduction in the focal depth of field was achieved compared to a single-aperture approach at 564 kHz. Short-burst (two-cycle) excitations in combination with PSK were found to suppress the formation of standing waves in ex vivo human thoracic vertebrae when focusing through the vertebral laminae. The results make an important contribution toward the development of a clinical-scale approach for targeting ultrasound therapy to the SC.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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 designBench or experimental
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

Citations28
Published2018
Admission routes2
Has abstractyes

Explore more

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