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Record W1872354477 · doi:10.1088/0031-9155/46/12/305

Interstitial ultrasound heating applicator for MR-guided thermal therapy.

2001· article· en· W1872354477 on OpenAlexaff
Rajiv Chopra, C. Luginbuhl, Alfred J. Weymouth, F. Stuart Foster, Michael Bronskill

Bibliographic record

VenuePhysics in Medicine and Biology · 2001
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer Institute
KeywordsMaterials scienceRADIUSUltrasoundThermalBiomedical engineeringAcousticsMedicinePhysics

Abstract

fetched live from OpenAlex

The ability to control the shape of thermal coagulation was investigated for various interstitial heating applicators incorporating planar transducers and device rotation. Magnetic-resonance-compatible interstitial ultrasound applicators were constructed and the effects of ultrasound power, frequency, scan rate and heating time on lesion radius were studied in heating experiments in excised liver tissue. Continuous thermal lesions were generated by scanning heating applicators over a 180 angular sector. The region of thermal coagulation was restricted to the prescribed sector. Lesion radius increased with acoustic power and heating time and decreased with increasing frequency. The relationship between the temperature distribution generated by the applicator and the resulting thermal lesion was assessed with MRI. Analysis of MR temperature maps revealed that the temperature distribution could be measured accurately within 2 mm from the surface of the applicator, and the boundary of thermal coagulation was defined by a temperature of 54 +/- 12 degrees C. Calculations of temperature distributions indicated that slower scan rates can overcome the tendency of perfusion to reduce the radius of thermal lesion. This applicator design and delivery strategy make conformal interstitial heating possible.

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 score0.816
Threshold uncertainty score0.316

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 head0.155
GPT teacher head0.361
Teacher spread0.206 · 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

Citations49
Published2001
Admission routes1
Has abstractyes

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