Stent implant follow-up in intravascular optical coherence tomography images

Abstract

The objectives of this article are (i) to utilize computer methods in detection of stent struts imaged in vivo by optical coherence tomography (OCT) during percutaneous coronary interventions (PCI); (ii) to provide measurements for the assessment and monitoring of in-stent restenosis by OCT post PCI. Thirty-nine OCT cross-sections from seven pullbacks from seven patients presenting varying degrees of neointimal hyperplasia (NIH) are selected, and stent struts are detected. Stent and lumen boundaries are reconstructed and one experienced observer analyzed the strut detection, the lumen and stent area measurements, as well as the NIH thickness in comparison to manual tracing using the reviewing software provided by the OCT manufacturer (LightLab Imaging, MA, USA). Very good agreements were found between the computer methods and the expert evaluations for lumen cross-section area (mean difference = 0.11 ± 0.70 mm(2); r (2) = 0.98, P < 0.0001) and the stent cross-section area (mean difference = 0.10 ± 1.28 mm(2); r (2) = 0.85, P value < 0.0001). The average number of detected struts was 10.4 ± 2.9 per cross-section when the expert identified 10.5 ± 2.8 (r (2) = 0.78, P value < 0.0001). For the given patient dataset: lumen cross-sectional area was on the average (6.05 ± 1.87 mm(2)), stent cross-sectional area was (6.26 ± 1.63 mm(2)), maximum angle between struts was on the average (85.96 ± 54.23°), maximum, average, and minimum distance between the stent and the lumen were (0.18 ± 0.13 mm), (0.08 ± 0.06 mm), and (0.01 ± 0.02 mm), respectively, and stent eccentricity was (0.80 ± 0.08). Low variability between the expert and automatic method was observed in the computations of the most important parameters assessing the degree of neointimal tissue growth in stents imaged by OCT pullbacks. After further extensive validation, the presented methods might offer a robust automated tool that will improve the evaluation and follow-up monitoring of in-stent restenosis in patients.

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.…”

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

“…1 Automated assessment can reduce this error and produce more consistent image interpretation. Various techniques have been reported for lumen segmentation, [1][2][3][4][5][6][7][8] stent strut detection, 7 and plaque characterization. 1,9 These approaches are either semi or fully automated.…”

“…The lumen geometry generated can then be refined using an active contour method (ACM). 6,8 The IV-OCT images in reports describing application of these lumen segmentation methods are usually selected to have optimal image quality. However, the real-world context of clinical IV-OCT frequently generates more challenging images resulting from complex anatomy, artifacts, and disease.…”

“…1(b)] that can be challenging for current segmentation tools. Morphological operations, 6 prior to the lumen segmentation, can be applied to the IV-OCT images to minimize the effect of blood artifacts. However, complete removal is not guaranteed, especially when blood is flowing very close to, or in continuity with the luminal wall.…”

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

“…Tomografia Ótica Coerente Intravascular (IOCT) é uma nova técnica de imagem médica que vem ganhando importância. Ela mede a intensidade das reflexões retro-espalhadas de luz infravermelha para gerar imagens tomográficas em tempo real (Unal et al, 2010). Apesar de IOCT ter baixa penetração através do sangue e dos tecidos (Puri et al, 2011), ele possui uma resolução de 10 µm, a qual é muito melhor em comparação com a resolução do Ultrassom intravascular (IVUS) (100-150 µm) (Bouma et al, 2003), permitindo detectar e caracterizar melhor a composição da placa aterosclerótica (Rieber et al, 2006).…”