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Record W2057519471 · doi:10.1088/0031-9155/54/11/009

Focused ultrasound treatment of VX2 tumors controlled by local harmonic motion

2009· article· en· W2057519471 on OpenAlexafffund
Laura Curiel, Yuexi Huang, Natalia Vykhodtseva, Kullervo Hynynen

Bibliographic record

VenuePhysics in Medicine and Biology · 2009
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsThunder Bay Regional Research InstituteHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Cancer InstituteNational Institutes of HealthTerry Fox Foundation
KeywordsUltrasoundMedicineAmplitudeSIGNAL (programming language)Biomedical engineeringTransducerHarmonicCoagulationNuclear medicineEcho (communications protocol)RadiologyPhysicsAcousticsOpticsComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the feasibility of using localized harmonic motion (LHM) to monitor and control focused ultrasound surgery (FUS) in VX2 tumors in vivo. FUS exposures were performed on 13 VX2 tumors implanted in nine rabbits. The same transducer induced coagulation and generated a localized oscillatory motion by periodically varying the radiation force. A separate diagnostic ultrasound transducer tracked motion by cross-correlating echo signals at different instances. A threshold in motion amplitude was instituted to cease exposure. Coagulation was confirmed by T2-weighted MR images, thermal dose obtained through MR thermometry and histological examinations. For tumor locations achieving coagulation, the LHM amplitude was 9% (p = 0.04) to 57% (p < 0.0001) lower than that before exposure. Control was successful for 74 (69%) out of 108 cases, with 52 (48%) reaching the threshold and achieving coagulation and 22 (21%) never reaching threshold nor coagulating. For the 34 (31%) unsuccessful exposures, 16 (15%) never reached the threshold but coagulation occurred, and 18 (16%) reached threshold without coagulation confirmed. Noise or radio-frequency signal changes explained motion over- or underestimation in 24 (22%) cases; the remaining 10 (9%) had other causes of error. The control was generally successful, but sudden change or noise in the acquired echo signal caused failure. Coagulation after exposure could be validated by comparing amplitudes before and after exposure.

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: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.254

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 communication0.0000.000
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 head0.063
GPT teacher head0.305
Teacher spread0.242 · 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

Citations30
Published2009
Admission routes2
Has abstractyes

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