Literature DB >> 31480007

A three-dimensional quantification of calcified and non-calcified plaques in coronary arteries based on computed tomography coronary angiography images: Comparison with expert's annotations and virtual histology intravascular ultrasound.

Vassiliki I Kigka1, Antonis Sakellarios1, Savvas Kyriakidis1, George Rigas1, Lambros Athanasiou2, Panagiotis Siogkas1, Panagiota Tsompou3, Dimitra Loggitsi4, Dominik C Benz5, Ronny Buechel5, Pedro A Lemos6, Gualtiero Pelosi7, Lampros K Michalis8, Dimitrios I Fotiadis9.   

Abstract

The detection, quantification and characterization of coronary atherosclerotic plaques has a major effect on the diagnosis and treatment of coronary artery disease (CAD). Different studies have reported and evaluated the noninvasive ability of Computed Tomography Coronary Angiography (CTCA) to identify coronary plaque features. The identification of calcified plaques (CP) and non-calcified plaques (NCP) using CTCA has been extensively studied in cardiovascular research. However, NCP detection remains a challenging problem in CTCA imaging, due to the similar intensity values of NCP compared to the perivascular tissue, which surrounds the vasculature. In this work, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of CP and NCP utilizing CTCA images and we compare the findings with virtual histology intravascular ultrasound (VH-IVUS) and manual expert's annotations. Bland-Altman analyses were employed to assess the agreement between the presented methodology and VH-IVUS. The assessment of the plaque volume, the lesion length and the plaque area in 18 coronary lesions indicated excellent correlation with VH-IVUS. More specifically, for the CP lesions the correlation of plaque volume, lesion length and plaque area was 0.93, 0.84 and 0.85, respectively, whereas the correlation of plaque volume, lesion length and plaque area for the NCP lesions was 0.92, 0.95 and 0.81, respectively. In addition to this, the segmentation of the lumen, CP and NCP in 1350 CTCA slices indicated that the mean value of DICE coefficient is 0.72, 0.7 and 0.62, whereas the mean HD value is 1.95, 1.74 and 1.95, for the lumen, CP and NCP, respectively.
Copyright © 2019. Published by Elsevier Ltd.

Entities:  

Keywords:  Active contour models; Atherosclerotic plaque; Calcified plaque; Computed tomography coronary angiography; Coronary artery disease; Non-calcified plaque; Segmentation

Mesh:

Year:  2019        PMID: 31480007     DOI: 10.1016/j.compbiomed.2019.103409

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  6 in total

1.  A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for In Silico Clinical Trials of Stent Design.

Authors:  Dimitrios Pleouras; Antonis Sakellarios; George Rigas; Georgia S Karanasiou; Panagiota Tsompou; Gianna Karanasiou; Vassiliki Kigka; Savvas Kyriakidis; Vasileios Pezoulas; George Gois; Nikolaos Tachos; Aidonis Ramos; Gualtiero Pelosi; Silvia Rocchiccioli; Lampros Michalis; Dimitrios I Fotiadis
Journal:  IEEE Open J Eng Med Biol       Date:  2021-05-20

2.  Coronary computed tomography angiography-based endothelial wall shear stress in normal coronary arteries.

Authors:  Jussi Schultz; Inge J van den Hoogen; Jurrien H Kuneman; Michiel A de Graaf; Vasileios Kamperidis; Alexander Broersen; J Wouter Jukema; Antonis Sakellarios; Sotirios Nikopoulos; Konstantina Tsarapatsani; Katerina Naka; Lampros Michalis; Dimitrios I Fotiadis; Teemu Maaniitty; Antti Saraste; Jeroen J Bax; Juhani Knuuti
Journal:  Int J Cardiovasc Imaging       Date:  2022-10-18       Impact factor: 2.316

Review 3.  Reporting Standards for a Bland-Altman Agreement Analysis: A Review of Methodological Reviews.

Authors:  Oke Gerke
Journal:  Diagnostics (Basel)       Date:  2020-05-22

4.  Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data.

Authors:  Dimitrios S Pleouras; Antonis I Sakellarios; Panagiota Tsompou; Vassiliki Kigka; Savvas Kyriakidis; Silvia Rocchiccioli; Danilo Neglia; Juhani Knuuti; Gualtiero Pelosi; Lampros K Michalis; Dimitrios I Fotiadis
Journal:  Sci Rep       Date:  2020-10-15       Impact factor: 4.379

5.  Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data.

Authors:  Vassiliki I Kigka; Eleni Georga; Vassilis Tsakanikas; Savvas Kyriakidis; Panagiota Tsompou; Panagiotis Siogkas; Lampros K Michalis; Katerina K Naka; Danilo Neglia; Silvia Rocchiccioli; Gualtiero Pelosi; Dimitrios I Fotiadis; Antonis Sakellarios
Journal:  Diagnostics (Basel)       Date:  2022-06-14

6.  Error Propagation in the Simulation of Atherosclerotic Plaque Growth and the Prediction of Atherosclerotic Disease Progression.

Authors:  Antonis I Sakellarios; Panagiotis Siogkas; Vassiliki Kigka; Panagiota Tsompou; Dimitrios Pleouras; Savvas Kyriakidis; Georgia Karanasiou; Gualtiero Pelosi; Sotirios Nikopoulos; Katerina K Naka; Silvia Rocchiccioli; Lampros K Michalis; Dimitrios I Fotiadis
Journal:  Diagnostics (Basel)       Date:  2021-12-08
  6 in total

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