ArticleJournal of medical imaging (Bellingham, Wash.)2016
Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Automated Artificial Intelligence Mapping of Coronary Plaque Calcification: A Comparison with Manual Intravascular Image Analysis.Journal of clinical medicine · 2025Article
- Method for parametric imaging of attenuation by intravascular optical coherence tomography.Biomedical optics express · 2021Article
- Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study.Journal of medical imaging (Bellingham, Wash.) · 2017Article
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10 authors.
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Abstract
Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Using realistic coronary artery disease phantoms, we determined insignificant differences in mean and standard deviation estimates between our pullback analyses and more conventional processing of stationary acquisitions with frame averaging. There was no effect of tissue depth or oblique imaging on pullback parameter estimates. The method's performance was assessed in comparison with observer-defined standards using clinical pullback data. Values (calcium [Formula: see text], lipid [Formula: see text], and fibrous [Formula: see text]) were consistent with previous measurements obtained by other means. Using optical parameters ([Formula: see text], [Formula: see text], [Formula: see text]), we achieved feature space separation of plaque types and classification accuracy of [Formula: see text]. Despite the rapid [Formula: see text] motion and varying incidence angle in pullbacks, the proposed computational pipeline appears to work as well as a more standard "stationary" approach.
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