title: Automatic stent strut detection in intravascular optical coherence tomographic pullback runs.
abstract: We developed and evaluated an automatic stent strut detection method in intravascular optical coherence tomography (IVOCT) pullback runs. Providing very high resolution images, IVOCT has been rapidly accepted as a coronary imaging modality for the optimization of the stenting procedure and its follow-up evaluation based on stent strut analysis. However, given the large number of struts visible in a pullback run, quantitative three-dimensional analysis is only feasible when the strut detection is performed automatically. The presented method first detects the candidate pixels using both a global intensity histogram and the intensity profile of each A-line. Gaussian smoothing is applied followed by specified Prewitt compass filters to detect the trailing shadow of each strut. Next, the candidate pixels are clustered using the shadow information. In the final step, several filters are applied to remove the false positives such as the guide wire. Our new method requires neither a priori knowledge of the strut status nor the lumen/vessel contours. In total, 10 IVOCT pullback runs from a 1-year follow-up study were used for validation purposes. 18,311 struts were divided into three strut status categories (malapposition, apposition or covered) and classified based on the image quality (high, medium or low). The inter-observer agreement is 95%. The sensitivity was defined as the ratio of the number of true positives and the total number of struts in the expert defined result. The proposed approach demonstrated an average sensitivity of 94%. For malapposed, apposed and covered stent struts, the sensitivity of the method is respectively 91, 93 and 94%, which shows the robustness towards different situations. The presented method can detect struts automatically regardless of the strut status or the image quality, and thus can be used for quantitative measurement, 3D reconstruction and visualization of the stents in IVOCT pullback runs.
authors.affiliation: LKEB-Division of Image Processing, Department of Radiology, Leiden University Medical Center, P. O. Box 9600, Leiden, The Netherlands.
authors.forename: Ancong
authors.initials: A
authors.lastname: Wang
authors.forename: Jeroen
authors.initials: J
authors.lastname: Eggermont
authors.forename: Niels
authors.initials: N
authors.lastname: Dekker
authors.forename: Hector M
authors.initials: HM
authors.lastname: Garcia-Garcia
authors.forename: Ravindra
authors.initials: R
authors.lastname: Pawar
authors.forename: Johan H C
authors.initials: JH
authors.lastname: Reiber
authors.forename: Jouke
authors.initials: J
authors.lastname: Dijkstra
mesh_terms: D000465:Algorithms; D001331:Automation; D016000:Cluster Analysis; D003331:Coronary Vessels / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D015233:Models, Statistical; D015588:Observer Variation; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects / Q000295:instrumentation*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D015607:Stents; D041623:Tomography, Optical Coherence; D016896:Treatment Outcome