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Record W2791031282 · doi:10.1117/12.2291164

Transurethral illumination probe design for deep photoacoustic imaging of prostate

2018· article· en· W2791031282 on OpenAlexaff
Мин Ай, Shuo Tang, Tim Salcudean, Robert Rohling, Purang Abolmaesumi

Bibliographic record

VenuePhotons Plus Ultrasound: Imaging and Sensing 2018 · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceImaging phantomOpticsFluencePenetration depthTransducerOptical fiberProstateCystoscopeLaserPhysicsMedicine

Abstract

fetched live from OpenAlex

Photoacoustic (PA) imaging with internal light illumination through optical fiber could enable imaging of internal organs at deep penetration. We have developed a transurethral probe with a multimode fiber inserted in a rigid cystoscope sheath for illuminating the prostate. At the distal end, the fiber tip is processed to diffuse light circumferentially over 2 cm length. A parabolic cylinder mirror then reflects the light to form a rectangular-shaped parallel beam which has at least 1 cm2 at the probe surface. The relatively large rectangular beam size can reduce the laser fluence rate on the urethral wall and thus reduce the potential of tissue damage. A 3 cm optical penetration in chicken tissue is achieved at a fluence rate around 7 mJ/cm2 . For further validation, a prostate phantom was built with similar optical properties of the human prostate. A 1.5 cm penetration depth is achieved in the prostate mimicking phantom at 10 mJ/cm2 fluence rate. PA imaging of prostate can potentially be carried out in the future by combining a transrectal ultrasound transducer and the transurethral illumination.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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