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Record W2007934576 · doi:10.1117/12.594586

3D reconstruction of an IVUS transducer trajectory with a single view in cineangiography

2005· article· en· W2007934576 on OpenAlexaff
Melissa Jourdain, Jean Meunier, Rosaire Mongrain, Denis Sherknies, Ji Yao Weng, Jean‐Claude Tardif

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsMontreal Heart InstituteMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsComputer scienceTransducerBiplaneIntravascular ultrasoundComputer visionTrajectoryArtificial intelligenceArtifact (error)StereoscopyPullbackOrientation (vector space)AcousticsRadiologyPhysicsMedicineMathematicsMaterials scienceGeometry

Abstract

fetched live from OpenAlex

During an Intravascular Ultrasound (IVUS) intervention, a catheter with an ultrasound transducer is introduced in the body through a blood vessel and then pulled back to image a sequence of vessel cross-sections. Unfortunately, there is no 3D information about the position and orientation of these cross-section planes. To position the IVUS images in space, some researchers have proposed complex stereoscopic procedures relying on biplane angiography to get two X-ray image sequences of the IVUS transducer trajectory along the catheter. We have elaborated a much simpler algorithm to recover the transducer 3D trajectory with only a single view X-ray image sequence. The known pullback distance of the transducer during the IVUS intervention is used as an a priori to perform this task. Considering that biplane system are difficult to operate and rather expensive and uncommon in hospitals; this simple pose estimation algorithm could lead to an affordable and useful tool to better assess the 3D shape of vessels investigated with IVUS.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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 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

Citations4
Published2005
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicCoronary Interventions and DiagnosticsFrench-language works237,207