MétaCan
Menu
Back to cohort
Record W2753893678 · doi:10.1364/oe.25.022164

Dual-beam manually-actuated distortion-corrected imaging (DMDI) with micromotor catheters

2017· article· en· W2753893678 on OpenAlexafffund
Anthony M. D. Lee, Geoffrey Hohert, Patricia T. Angkiriwang, Calum MacAulay, Pierre Lane

Bibliographic record

VenueOptics Express · 2017
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsBC Cancer Agency
FundersBC Cancer AgencyNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsImaging phantomDistortion (music)OpticsCalibrationBeam (structure)PhysicsComputer scienceMedical imagingComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

We present a new paradigm for performing two-dimensional scanning called dual-beam manually-actuated distortion-corrected imaging (DMDI). DMDI operates by imaging the same object with two spatially-separated beams that are being mechanically scanned rapidly in one dimension with slower manual actuation along a second dimension. Registration of common features between the two imaging channels allows remapping of the images to correct for distortions due to manual actuation. We demonstrate DMDI using a 4.7 mm OD rotationally scanning dual-beam micromotor catheter (DBMC). The DBMC requires a simple, one-time calibration of the beam paths by imaging a patterned phantom. DMDI allows for distortion correction of non-uniform axial speed and rotational motion of the DBMC. We show the utility of this technique by demonstrating en face OCT image distortion correction of a manually-scanned checkerboard phantom and fingerprint scan.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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 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

Citations8
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueOptics ExpressSame topicOptical Coherence Tomography ApplicationsFrench-language works237,207