A New 3-D Automated Computational Method to Evaluate In-Stent Neointimal Hyperplasia in In-Vivo Intravascular Optical Coherence Tomography Pullbacks

Abstract

Abstract. Detection of stent struts imaged in vivo by optical coherence tomography (OCT) after percutaneous coronary interventions (PCI) and quantification of in-stent neointimal hyperplasia (NIH) are important. In this paper, we present a new computational method to facilitate the physician in this endeavor to assess and compare new (drug-eluting) stents. We developed a new algorithm for stent strut detection and utilized splines to reconstruct the lumen and stent boundaries which provide automatic measurements of NIH thickness, lumen and stent area. Our original approach is based on the detection of stent struts unique characteristics: bright reflection and shadow behind. Furthermore, we present for the first time to our knowledge a rotation correction method applied across OCT cross-section images for 3D reconstruction and visualization of reconstructed lumen and stent boundaries for further analysis in the longitudinal dimension of the coronary artery. Our experiments over OCT cross-sections taken from 7 patients presenting varying degrees of NIH after PCI illustrate a good agreement between the computer method and expert evaluations: Bland-Altmann analysis revealed a mean difference for lumen cross-section area of 0.11 ± 0.70mm 2 and for the stent cross-section area of 0.10 ± 1.28mm 2 .

Automatic detection of bioresorbable vascular scaffold struts in intravascular optical coherence tomography pullback runs

“…Many automated metallic stent strut detection methods [16][17][18][19] have been published. However, to the best of our knowledge, current BVS analyses in IVOCT images still rely on the labor intensive manual delineation of struts.…”

Fully automated side branch detection in intravascular optical coherence tomography pullback runs

“…Currently, only 38% of the peerreviewed papers present correctly calibrated images [29]. Lumen contour detection is one of most common image segmentation steps and the published methods include Markov random field and wavelet transform analysis [28], fuzzy C means clustering and wavelet transform [33], deformable spline models [34] and dynamic programming [35]. However, in many cases, only a light-weight and fast lumen contour estimation like our method is needed.…”

Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica

“…Como foi discutido anteriormente, a literatura mostra alguns enfoques computacionais para a segmentação do lúmen na coronária em imagens IOCT (Dubuisson et al, 2009;Gurmeric et al, 2009;Sihan et al, 2008;Tsantis et al, 2012;Tung et al, 2011). Contudo, apesar dos esforços, a ausência de um número razoável de métodos, e resultados mais acurados dos mesmos, faz com que a procura de métodos alternativos de segmentação da coronária continue.…”

“…No trabalho de Tung et al (2011), os autores fazem a segmentação do lúmen da coronária utilizando uma combinação de um algoritmo de maximização de Expectativa (Dempster et al, 1977), um algoritmo Graph Cuts (Boykov e Jolly, 2001) e contornos ativos (Kass et al, 1988). No método proposto por Gurmeric et al (2009), os pesquisadores utilizam contornos ativos que se propagam com equação diferencial ordinária (EDO) para atingir uma ótima solução. Dubuisson et al (2009) propõem um método no qual combina binarização por Otsu (1979), segmentação morfológica e contornos ativos.…”