Automated detection and quantification of clusters of malapposed and uncovered intracoronary stent struts assessed with optical coherence tomography
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.
KeywordsOptical coherence tomography Coronary stent Drug-eluting stents Strut apposition Cluster of malapposition Cluster of uncovered struts
The research program is supported by Grant G.0690.09N of the Research Foundation-Flanders (FWO) and IOF (Industrial research Foundation) grant of the Catholic University Leuven ZKC2992. Tom Adriaenssens is supported partially by a Clinical Doctoral Grant of the Research Foundation Flanders.
Conflict of interest
The authors have no conflict of interest to declare. Tom Adriaenssens received consultancy and speaker’s fees for St Jude Medical.
- 1.Cook S, Wenaweser P, Togni M, Billinger M, Morger C, Seiler C, Vogel R, Hess O, Meier B, Windecker S (2007) Incomplete stent apposition and very late stent thrombosis after drug-eluting stent implantation. Circulation 115:2426–2434. doi: 10.1161/circulationaha.106.658237 PubMedCrossRefGoogle Scholar
- 2.Ughi GJ, Adriaenssens T, Onsea K, Kayaert P, Dubois C, Sinnaeve P, Coosemans M, Desmet W, D’Hooge J (2012) Automatic segmentation of in vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage. Int J Cardiovasc Imaging 28:229–241. doi: 10.1007/s10554-011-9824-3 PubMedCrossRefGoogle Scholar
- 3.Imola F, Mallus MT, Ramazzotti V, Manzoli A, Pappalardo A, Di Giorgio A, Albertucci M, Prati F (2010) Safety and feasibility of frequency domain optical coherence tomography to guide decision making in percutaneous coronary intervention. EuroIntervention 6:575–581. doi: 10.4244/eijv6i5a97 PubMedCrossRefGoogle Scholar
- 6.Ughi G, Vandyck C, Adriaenssens T, Hoymans V, Sinnaeve P, Timmermans J, Desmet W, Vrints C, D’Hooge J (2013) Automatic assessment of stent neointimal coverage by intravascular optical coherence tomography. Eur Heart J CardiovascImaging. doi: 10.1093/ehjci/jet134
- 8.Tan PSMKV (2005) Introduction to data mining. Addison-Wesley publishing company, Boston, USA, pp 487–568Google Scholar