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Record W2419288218 · doi:10.1088/0031-9155/61/1/23

Comparison of analytical and numerical approaches for CT-based aberration correction in transcranial passive acoustic imaging

2015· article· en· W2419288218 on OpenAlexaff
Ryan M. Jones, Kullervo Hynynen

Bibliographic record

VenuePhysics in Medicine and Biology · 2015
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsBeamformingAcousticsPhysicsImage qualityCenter frequencyTomographyOpticsMain lobeComputer scienceImage (mathematics)Artificial intelligenceAntenna (radio)Telecommunications

Abstract

fetched live from OpenAlex

Computed tomography (CT)-based aberration corrections are employed in transcranial ultrasound both for therapy and imaging. In this study, analytical and numerical approaches for calculating aberration corrections based on CT data were compared, with a particular focus on their application to transcranial passive imaging. Two models were investigated: a three-dimensional full-wave numerical model (Connor and Hynynen 2004 IEEE Trans. Biomed. Eng. 51 1693-706) based on the Westervelt equation, and an analytical method (Clement and Hynynen 2002 Ultrasound Med. Biol. 28 617-24) similar to that currently employed by commercial brain therapy systems. Trans-skull time delay corrections calculated from each model were applied to data acquired by a sparse hemispherical (30 cm diameter) receiver array (128 piezoceramic discs: 2.5 mm diameter, 612 kHz center frequency) passively listening through ex vivo human skullcaps (n = 4) to emissions from a narrow-band, fixed source emitter (1 mm diameter, 516 kHz center frequency). Measurements were taken at various locations within the cranial cavity by moving the source around the field using a three-axis positioning system. Images generated through passive beamforming using CT-based skull corrections were compared with those obtained through an invasive source-based approach, as well as images formed without skull corrections, using the main lobe volume, positional shift, peak sidelobe ratio, and image signal-to-noise ratio as metrics for image quality. For each CT-based model, corrections achieved by allowing for heterogeneous skull acoustical parameters in simulation outperformed the corresponding case where homogeneous parameters were assumed. Of the CT-based methods investigated, the full-wave model provided the best imaging results at the cost of computational complexity. These results highlight the importance of accurately modeling trans-skull propagation when calculating CT-based aberration corrections. Although presented in an imaging context, our results may also be applicable to the problem of transmit focusing through the skull.

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)0.002
metaresearch head score (Gemma)0.014
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
GPT teacher head0.398
Teacher spread0.201 · 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

Citations67
Published2015
Admission routes1
Has abstractyes

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