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Record W4236842989 · doi:10.32920/ryerson.14652363.v1

High speed rotary system for catheter based 3-D imaging with optical coherence tomography (OCT)

2021· preprint· en· W4236842989 on OpenAlexaff
Antonio Di Mauro

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsOptical coherence tomographyFrame rateBiomedical engineeringMagnetic resonance imagingTomographyUltrasoundMaterials scienceOpticsPhysicsRadiologyMedicineAcoustics

Abstract

fetched live from OpenAlex

Optical Coherence Tomography (OCT) is an addition to the other tomographic imaging techniques of x-ray computed tomography, magnetic resonance imaging, and ultrasound imaging. OCT uses optical reflections of biological tissues as opposed to x-rays, RF fields, and sound waves to obtain images. A rotary and pullback system has been developed for use with OCT. The system was developed to facilitate the three dimensional imaging of various lumens in humans and animals. The system is capable of rotating at a rate of 200 Hz. At this rate the rotary system will allow for a frame acquisition rate of 200 fps which is significantly higher than the highest published acquisition rate to date of 108 fps. The probes used with the system were modeled after the Intravascular Ultrasound (IVUS) miniature torque cable design. The probes can be sealed and sterilized between subjects without being damaged; unlike the single use IVUS probes. The rotary system was used to image the outer ear of a mouse in vivo. A lateral slice from the resulting three dimensional image was compared to the general histology of a mouse ear. The image compared well to the general anatomy as found on the histology.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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