title: Automated detection and quantification of clusters of malapposed and uncovered intracoronary stent struts assessed with optical coherence tomography.
abstract: To date, accurate quantification and localization of malapposed and uncovered struts needs manual and time consuming analysis of large datasets. To develop an algorithm for automated detection and quantification of clusters of malapposed and uncovered struts in optical coherence tomography (OCT) pullbacks, including comprehensive information about their three-dimensional spatial distribution. 64 lesions in 64 patients treated with drug-eluting stent underwent assessment with OCT immediately after implantation and at 9-month follow-up (55 patients). An automated algorithm was used to detect and quantify stent strut malapposition at baseline and coverage at follow-up on an individual strut level. We subsequently applied an algorithm for the automated clustering of malapposed and uncovered struts and for the quantification of clusters' properties. In the 64 baseline examinations, a total of 24,013 struts were analyzed, of which 1,519 (6 %) were malapposed. Most malapposed struts (78 %) occurred in clusters and more than half of patients had malapposition clusters. The mean number of struts per cluster was 19.7 ± 11.8 with a mean malapposition distance of 213 ± 66 μm. In the 55 follow-up pullbacks, a total of 20,484 struts were analyzed, of which 1,320 (6 %) were uncovered. Again, most uncovered struts (85 %) occurred in clusters. The mean number of struts per cluster was 21.1 ± 14.7. We developed an automated algorithm for studying clustering of malapposed or uncovered struts. This algorithm might facilitate future investigations of the prognostic impact of clusters of malapposed or uncovered struts.
authors.affiliation: Department of Cardiovascular Medicine, University Hospitals Leuven, Louvain, Belgium, tom.adriaenssens@uzleuven.be.
authors.forename: Tom
authors.initials: T
authors.lastname: Adriaenssens
authors.forename: Giovanni J
authors.initials: GJ
authors.lastname: Ughi
authors.forename: Christophe
authors.initials: C
authors.lastname: Dubois
authors.forename: Kevin
authors.initials: K
authors.lastname: Onsea
authors.forename: Dries
authors.initials: D
authors.lastname: De Cock
authors.forename: Johan
authors.initials: J
authors.lastname: Bennett
authors.forename: Stefanus
authors.initials: S
authors.lastname: Wiyono
authors.forename: Maarten
authors.initials: M
authors.lastname: Vanhaverbeke
authors.forename: Peter
authors.initials: P
authors.lastname: Sinnaeve
authors.forename: Ann
authors.initials: A
authors.lastname: Belmans
authors.forename: Jan
authors.initials: J
authors.lastname: D'hooge
authors.forename: Walter
authors.initials: W
authors.lastname: Desmet
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D017023:Coronary Angiography; D003327:Coronary Disease / Q000188:drug therapy*; D054855:Drug-Eluting Stents / Q000009:adverse effects*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D011475:Prosthesis Failure / Q000009:adverse effects*; D041623:Tomography, Optical Coherence