Cargando…

Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We ar...

Descripción completa

Detalles Bibliográficos
Autores principales: Hadjiiski, Lubomir, Liu, Jordan, Chan, Heang-Ping, Zhou, Chuan, Wei, Jun, Chughtai, Aamer, Kuriakose, Jean, Agarwal, Prachi, Kazerooni, Ella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5046098/
https://www.ncbi.nlm.nih.gov/pubmed/27721896
http://dx.doi.org/10.1155/2016/1835297
_version_ 1782457231218835456
author Hadjiiski, Lubomir
Liu, Jordan
Chan, Heang-Ping
Zhou, Chuan
Wei, Jun
Chughtai, Aamer
Kuriakose, Jean
Agarwal, Prachi
Kazerooni, Ella
author_facet Hadjiiski, Lubomir
Liu, Jordan
Chan, Heang-Ping
Zhou, Chuan
Wei, Jun
Chughtai, Aamer
Kuriakose, Jean
Agarwal, Prachi
Kazerooni, Ella
author_sort Hadjiiski, Lubomir
collection PubMed
description The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist's reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.
format Online
Article
Text
id pubmed-5046098
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-50460982016-10-09 Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography Hadjiiski, Lubomir Liu, Jordan Chan, Heang-Ping Zhou, Chuan Wei, Jun Chughtai, Aamer Kuriakose, Jean Agarwal, Prachi Kazerooni, Ella Comput Math Methods Med Research Article The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist's reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases. Hindawi Publishing Corporation 2016 2016-09-19 /pmc/articles/PMC5046098/ /pubmed/27721896 http://dx.doi.org/10.1155/2016/1835297 Text en Copyright © 2016 Lubomir Hadjiiski et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hadjiiski, Lubomir
Liu, Jordan
Chan, Heang-Ping
Zhou, Chuan
Wei, Jun
Chughtai, Aamer
Kuriakose, Jean
Agarwal, Prachi
Kazerooni, Ella
Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography
title Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography
title_full Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography
title_fullStr Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography
title_full_unstemmed Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography
title_short Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography
title_sort best-quality vessel identification using vessel quality measure in multiple-phase coronary ct angiography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5046098/
https://www.ncbi.nlm.nih.gov/pubmed/27721896
http://dx.doi.org/10.1155/2016/1835297
work_keys_str_mv AT hadjiiskilubomir bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT liujordan bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT chanheangping bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT zhouchuan bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT weijun bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT chughtaiaamer bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT kuriakosejean bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT agarwalprachi bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography
AT kazerooniella bestqualityvesselidentificationusingvesselqualitymeasureinmultiplephasecoronaryctangiography