An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.

A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting im...

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Autores principales:	Ronald van 't Klooster, Andrew J Patterson, Victoria E Young, Jonathan H Gillard, Johan H C Reiber, Rob J van der Geest
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/95dd8cae8c5942f9ba6743ec16f1833d
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spelling	oai:doaj.org-article:95dd8cae8c5942f9ba6743ec16f1833d2021-11-18T08:49:43ZAn objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.1932-620310.1371/journal.pone.0078492https://doaj.org/article/95dd8cae8c5942f9ba6743ec16f1833d2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24194941/?tool=EBIhttps://doaj.org/toc/1932-6203A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions.Ronald van 't KloosterAndrew J PattersonVictoria E YoungJonathan H GillardJohan H C ReiberRob J van der GeestPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 10, p e78492 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ronald van 't Klooster Andrew J Patterson Victoria E Young Jonathan H Gillard Johan H C Reiber Rob J van der Geest An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
description	A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions.
format	article
author	Ronald van 't Klooster Andrew J Patterson Victoria E Young Jonathan H Gillard Johan H C Reiber Rob J van der Geest
author_facet	Ronald van 't Klooster Andrew J Patterson Victoria E Young Jonathan H Gillard Johan H C Reiber Rob J van der Geest
author_sort	Ronald van 't Klooster
title	An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
title_short	An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
title_full	An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
title_fullStr	An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
title_full_unstemmed	An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
title_sort	objective method to optimize the mr sequence set for plaque classification in carotid vessel wall images using automated image segmentation.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/95dd8cae8c5942f9ba6743ec16f1833d
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An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.

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