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.
Highlights
Heart disease is a leading cause of death in the developed countries and coronary artery disease (CAD) is the most common form [1]
We developed and evaluated an automatic stent strut detection method in intravascular optical coherence tomography (IVOCT) pullback runs
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 and classified based on the image quality
Summary
Heart disease is a leading cause of death in the developed countries and coronary artery disease (CAD) is the most common form [1]. The first generation of stents were bare metal stents, which have proven to be associated with an increased risk of coronary restenosis during the vessel wall healing process based on long term follow up studies [2, 3]. The second generation—drug eluting stents (DES) significantly decreased the occurrence of restenosis, but they are associated with late acquired stent malapposition which may lead to in-stent thrombosis [4]. Newly implanted stents usually are located at the lumen boundary without tissue coverage (apposition) and later on nicely covered with a thin layer of tissue, still acute malapposition may occur or they may
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