Automatic detection of coronary stent struts in intravascular OCT imaging

Optical coherence tomography (OCT) is a light-based, high resolution imaging technique to guide stent deployment procedure for stenosis. OCT can accurately differentiate the most superficial layers of the vessel wall as well as stent struts and the vascular tissue surrounding them. In this paper, we automatically detect the struts of coronary stents present in OCT sequences. We propose a novel method to detect the strut shadow zone and accurately segment and reconstruct the strut in 3D. The estimation of the position of the strut shadow zone is the key requirement which enables the strut segmentation. After identification of the shadow zone we use probability map to estimate stent strut positions. This method can be applied to cross-sectional OCT images to detect the struts. Validation is performed using simulated data as well as in four in-vivo OCT sequences and the accuracy of strut detection is over 90%. The comparison against manual expert segmentation demonstrates that the proposed strut identification is robust and accurate.