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Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction
Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of mor...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149962/ https://www.ncbi.nlm.nih.gov/pubmed/35645394 http://dx.doi.org/10.3390/tomography8030108 |
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author | Carpenter, Harry J. Ghayesh, Mergen H. Zander, Anthony C. Li, Jiawen Di Giovanni, Giuseppe Psaltis, Peter J. |
author_facet | Carpenter, Harry J. Ghayesh, Mergen H. Zander, Anthony C. Li, Jiawen Di Giovanni, Giuseppe Psaltis, Peter J. |
author_sort | Carpenter, Harry J. |
collection | PubMed |
description | Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation. |
format | Online Article Text |
id | pubmed-9149962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91499622022-05-31 Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction Carpenter, Harry J. Ghayesh, Mergen H. Zander, Anthony C. Li, Jiawen Di Giovanni, Giuseppe Psaltis, Peter J. Tomography Review Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation. MDPI 2022-05-17 /pmc/articles/PMC9149962/ /pubmed/35645394 http://dx.doi.org/10.3390/tomography8030108 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Carpenter, Harry J. Ghayesh, Mergen H. Zander, Anthony C. Li, Jiawen Di Giovanni, Giuseppe Psaltis, Peter J. Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction |
title | Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction |
title_full | Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction |
title_fullStr | Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction |
title_full_unstemmed | Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction |
title_short | Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction |
title_sort | automated coronary optical coherence tomography feature extraction with application to three-dimensional reconstruction |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149962/ https://www.ncbi.nlm.nih.gov/pubmed/35645394 http://dx.doi.org/10.3390/tomography8030108 |
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