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Record W2090354876 · doi:10.1117/1.3606566

Coregistered photoacoustic-ultrasound imaging applied to brachytherapy

2011· letter· en· W2090354876 on OpenAlexafffund
Tyler Harrison, Roger J. Zemp

Bibliographic record

VenueJournal of Biomedical Optics · 2011
Typeletter
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteAlberta InnovatesCanada Foundation for InnovationMinistry of Advanced Education, Government of AlbertaUniversity of AlbertaCancer Research Institute
KeywordsBrachytherapyPhotoacoustic imaging in biomedicineDosimetryUltrasoundImaging phantomMedical imagingMaterials scienceRadiation therapyBiomedical engineeringNuclear medicineMedical physicsMedicineRadiologyOpticsPhysics

Abstract

fetched live from OpenAlex

Brachytherapy is a form of radiation therapy commonly used in the treatment of prostate cancer wherein sustained radiation doses can be precisely targeted to the tumor area by the implantation of small radioactive seeds around the treatment area. Ultrasound is a popular imaging mode for seed implantation, but the seeds are difficult to distinguish from the tissue structure. In this work, we demonstrate the feasibility of photoacoustic imaging for identifying brachytherapy seeds in a tissue phantom, comparing the received intensity to endogenous contrast. We have found that photoacoustic imaging at 1064 nm can identify brachytherapy seeds uniquely at laser penetration depths of 5 cm in biological tissue at the ANSI limit for human exposure with a contrast-to-noise ratio of 26.5 dB. Our realtime combined photoacoustic-ultrasound imaging approach may be suitable for brachytherapy seed placement and post-placement verification, potentially allowing for realtime dosimetry assessment during implantation.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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
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

Citations48
Published2011
Admission routes2
Has abstractyes

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