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Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming.

Zahnd G, Karanasos A, van Soest G, Regar E, Niessen W, Gijsen F, van Walsum T - Int J Comput Assist Radiol Surg (2015)

Bottom Line: In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure.However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands, g.zahnd@erasmusmc.nl.

ABSTRACT

Objectives: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.

Methods: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients.

Results: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22 ± 18 μm) and were similar to inter-observer reproducibility (21 ± 19 μm, R = .74), while being significantly faster and fully reproducible.

Conclusion: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.

No MeSH data available.


Related in: MedlinePlus

Segmentation framework. a Cartoon depicting the region of interest (ROI, dashed lines) encompassing the fibrous cap. b OCT image of an in vivo human coronary artery, in Cartesian coordinates, with the resulting luminal (cyan line) and abluminal (magenta line) segmentation contours. c ROI in polar coordinates, with the luminal contour (cyan line). d Gradient image . e Transformed cost image . f Cumulated cost , with the optimal path (magenta line). g Resulting abluminal segmentation contour
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Fig1: Segmentation framework. a Cartoon depicting the region of interest (ROI, dashed lines) encompassing the fibrous cap. b OCT image of an in vivo human coronary artery, in Cartesian coordinates, with the resulting luminal (cyan line) and abluminal (magenta line) segmentation contours. c ROI in polar coordinates, with the luminal contour (cyan line). d Gradient image . e Transformed cost image . f Cumulated cost , with the optimal path (magenta line). g Resulting abluminal segmentation contour

Mentions: Coronary artery disease is the most common cause of human mortality and morbidity in industrialized countries. Acute coronary syndrome (ACS), the most severe manifestation of atherosclerotic disease, is principally caused by acute coronary thrombosis, which is mainly provoked by plaque rupture [17]. The morphological characteristics of such plaques that are prone to rupture (also dubbed “high-risk” or “vulnerable” plaques) are (1) a large lipid necrotic core, (2) an overlying thin fibrous cap, and (3) dense macrophage infiltration (Fig. 1a) [4]. These plaques are also known as thin-cap fibroatheromas (TCFAs) and are considered the precursor phenotype of plaque rupture. The most critical component of plaque stability is fibrous cap thickness, i.e., thinner caps being more prone to rupture than thicker caps, and the threshold of has been widely adopted to identify high-risk lesions [11]. Accordingly, identification of vulnerable plaques could potentially guide appropriate surgical treatments such as percutaneous coronary intervention (e.g., balloon angioplasty or stent placement) prior to the occurrence of an event. Therefore, in vivo quantification of fibrous cap thickness represents a major clinical challenge.Fig. 1


Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming.

Zahnd G, Karanasos A, van Soest G, Regar E, Niessen W, Gijsen F, van Walsum T - Int J Comput Assist Radiol Surg (2015)

Segmentation framework. a Cartoon depicting the region of interest (ROI, dashed lines) encompassing the fibrous cap. b OCT image of an in vivo human coronary artery, in Cartesian coordinates, with the resulting luminal (cyan line) and abluminal (magenta line) segmentation contours. c ROI in polar coordinates, with the luminal contour (cyan line). d Gradient image . e Transformed cost image . f Cumulated cost , with the optimal path (magenta line). g Resulting abluminal segmentation contour
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4563002&req=5

Fig1: Segmentation framework. a Cartoon depicting the region of interest (ROI, dashed lines) encompassing the fibrous cap. b OCT image of an in vivo human coronary artery, in Cartesian coordinates, with the resulting luminal (cyan line) and abluminal (magenta line) segmentation contours. c ROI in polar coordinates, with the luminal contour (cyan line). d Gradient image . e Transformed cost image . f Cumulated cost , with the optimal path (magenta line). g Resulting abluminal segmentation contour
Mentions: Coronary artery disease is the most common cause of human mortality and morbidity in industrialized countries. Acute coronary syndrome (ACS), the most severe manifestation of atherosclerotic disease, is principally caused by acute coronary thrombosis, which is mainly provoked by plaque rupture [17]. The morphological characteristics of such plaques that are prone to rupture (also dubbed “high-risk” or “vulnerable” plaques) are (1) a large lipid necrotic core, (2) an overlying thin fibrous cap, and (3) dense macrophage infiltration (Fig. 1a) [4]. These plaques are also known as thin-cap fibroatheromas (TCFAs) and are considered the precursor phenotype of plaque rupture. The most critical component of plaque stability is fibrous cap thickness, i.e., thinner caps being more prone to rupture than thicker caps, and the threshold of has been widely adopted to identify high-risk lesions [11]. Accordingly, identification of vulnerable plaques could potentially guide appropriate surgical treatments such as percutaneous coronary intervention (e.g., balloon angioplasty or stent placement) prior to the occurrence of an event. Therefore, in vivo quantification of fibrous cap thickness represents a major clinical challenge.Fig. 1

Bottom Line: In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure.However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands, g.zahnd@erasmusmc.nl.

ABSTRACT

Objectives: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.

Methods: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients.

Results: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22 ± 18 μm) and were similar to inter-observer reproducibility (21 ± 19 μm, R = .74), while being significantly faster and fully reproducible.

Conclusion: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.

No MeSH data available.


Related in: MedlinePlus