224 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author유홍기-
dc.date.accessioned2018-02-05T00:36:38Z-
dc.date.available2018-02-05T00:36:38Z-
dc.date.issued2016-03-
dc.identifier.citationMEDICAL PHYSICS, v. 43, NO 4, Page. 1662-1675en_US
dc.identifier.issn0094-2405-
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1118/1.4943374/abstract;jsessionid=44345D5CEBBFFAE7E609E4802F888596.f02t03-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/35360-
dc.description.abstractPurpose: Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images. Methods: The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers. Results: Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R ˃ 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 mu m, respectively) and a narrow range of limits of agreement (36 and 54 mu m, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage. Conclusions: The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including instent thrombosis and in-stent restenosis. (C) 2016 American Association of Physicists in Medicine.en_US
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (Nos. NRF-2015R1A1A1A05027209 and NRF-2015R1A2A2A07027863).en_US
dc.language.isoenen_US
dc.publisherAMER ASSOC PHYSICISTS MEDICINE AMER INST PHYSICSen_US
dc.subjectoptical coherence tomographyen_US
dc.subjectstent thrombosisen_US
dc.subjectimage segmentationen_US
dc.subjectartificial neural networken_US
dc.subjectstent malappositionen_US
dc.subjectneointimal coveargeen_US
dc.titleAutomated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverageen_US
dc.typeArticleen_US
dc.relation.no4-
dc.relation.volume43-
dc.identifier.doi10.1118/1.4943374-
dc.relation.page1662-1675-
dc.relation.journalMEDICAL PHYSICS-
dc.contributor.googleauthorNam, Hyeong Soo-
dc.contributor.googleauthorKim, Chang-Soo-
dc.contributor.googleauthorLee, Jae Joong-
dc.contributor.googleauthorSong, Joon Woo-
dc.contributor.googleauthorKim, Jin Won-
dc.contributor.googleauthorYoo, Hongki-
dc.relation.code2016001264-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING-
dc.identifier.pidhyoo-
dc.identifier.orcidhttp://orcid.org/0000-0001-9819-3135-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE