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Record W2883732662 · doi:10.1109/tmi.2018.2855166

Toward Intra-Operative Prostate Photoacoustic Imaging: Configuration Evaluation and Implementation Using the da Vinci Research Kit

2018· article· en· W2883732662 on OpenAlexafffund
Hamid Moradi, Shuo Tang, Septimiu E. Salcudean

Bibliographic record

VenueIEEE Transactions on Medical Imaging · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsTransducerDirectivityComputer scienceData acquisitionPickupProstatePhotoacoustic imaging in biomedicineAcousticsComputer visionArtificial intelligenceOpticsPhysicsMedicineTelecommunicationsImage (mathematics)

Abstract

fetched live from OpenAlex

We compare different possible scanning geometries for prostate photoacoustic tomography (PAT) while considering a realistic reconstruction scenario in which the limited view of the prostate and the directivity effect of the transducer are considered. Simulations and experiments confirm that an intra-operative configuration in which the photoacoustic signal is received by a pickup transducer from the anterior surface of the prostate provides the best approach. We propose a PAT acquisition system that includes a da Vinci system controlled by the da Vinci Research Kit, an illumination laser, and an ultrasound machine with parallel data acquisition. The robot maneuvers the pickup transducer to form a cylindrical detection surface around the prostate. The robot is programmed to acquire trajectories in which the transducer face is parallel to and oriented toward a rotational tomography axis, while the laser is fired and PAT data are collected at regular intervals. We present our initial images acquired with this novel system.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.386
Teacher spread0.335 · 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 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

Citations33
Published2018
Admission routes2
Has abstractyes

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