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Record W7132976315

Validation of magnetic resonance imaging of ultrasound fields

2004· dissertation· W7132976315 on OpenAlexfundno aff
Jennifer Wai Evans

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrophoneUltrasoundUltrasonic sensorMagnetic resonance imagingMedical ultrasoundSound pressureMagnetic field
DOInot available

Abstract

fetched live from OpenAlex

Ultrasonic imaging is commonly used in medical diagnostic and therapeutic applications. The ability of ultrasound to achieve these tasks is determined by the acoustic field generated within the target object necessitating an understanding of the interaction of ultrasound with tissue. The purpose of this thesis is to validate the MR pressure measurements against conventional hydrophone measurements of ultrasonic pressure. Good agreement was found between the MR and hydrophone values. The verification of the quantitative nature of the MR technique enables its use in investigations of other ultrasound phenomena. A novel magnetic resonance (MR) imaging method has recently been described that allows non-invasive, quantitative mapping of medical ultrasound fields in tissue. A strong magnetic field gradient resonant with the applied ultrasound frequency is required to detect the motions associated with the ultrasound. A direct measurement of absolute pressure of the ultrasound wave can theoretically be obtained from the measured displacements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.009
GPT teacher head0.291
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2004
Admission routes1
Has abstractyes

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