Stent implant follow-up in intravascular optical coherence tomography images

Int J Cardiovasc Imaging. 2010 Oct;26(7):809-16. doi: 10.1007/s10554-009-9508-4. Epub 2009 Sep 24.


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.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms
  • Angioplasty, Balloon, Coronary / adverse effects
  • Angioplasty, Balloon, Coronary / instrumentation*
  • Automation, Laboratory
  • Coronary Restenosis / diagnosis*
  • Coronary Restenosis / etiology
  • Humans
  • Hyperplasia
  • Image Interpretation, Computer-Assisted
  • Predictive Value of Tests
  • Reproducibility of Results
  • Severity of Illness Index
  • Software
  • Stents*
  • Time Factors
  • Tomography, Optical Coherence*
  • Treatment Outcome