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Record W4248686123 · doi:10.32920/ryerson.14652363

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

2021· preprint· en· W4248686123 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 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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.004

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 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
GenreMethods

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

Citations2
Published2021
Admission routes1
Has abstractyes

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