MétaCan
Menu
Back to cohort
Record W2726936488 · doi:10.1088/1361-6560/aa7cd5

Design of patient-specific focused ultrasound arrays for non-invasive brain therapy with increased trans-skull transmission and steering range

2017· article· en· W2726936488 on OpenAlexafffund
Alec Hughes, Kullervo Hynynen

Bibliographic record

VenuePhysics in Medicine and Biology · 2017
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthCanada Research Chairs
KeywordsFocused ultrasoundSkullUltrasoundPhased arrayTransducerUltrasound energyBeam steeringTransmission (telecommunications)Phase (matter)Biomedical engineeringMedicineAcousticsComputer scienceRadiologyBeam (structure)SurgeryOpticsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

The use of a phased array of ultrasound transducer elements to sonicate through the skull has opened the way for new treatments and the delivery of therapeutics beyond the blood-brain barrier. The limited steering range of current clinical devices, particularly at higher frequencies, limits the regions of the brain that are considered treatable by ultrasound. A new array design is introduced that allows for high levels of beam steering and increased transmission throughout the brain. These improvements are achieved using concave transducers normal to the outer-skull surface in a patient-specific configuration to target within the skull, so that the far-field of each beam is within the brain. It is shown that by using pulsed ultrasound waves timed to arrive in-phase at the desired target, sufficient levels of acoustic energy are delivered for blood-brain barrier opening throughout the brain.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.090
GPT teacher head0.292
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations29
Published2017
Admission routes2
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207