Coronary Vascular Stent Detection Based On OCT Image And Its Three - Dimensional Reconstruction

Cardiovascular disease is one of the leading cause of human morbidity and mortality. The progress of technology has improved the diagnosis and treatment of cardiovascular diseases. Coronary artery stent implantation is one of such surgical techniques. During the surgery, a stent is placed in the coronary artery of patients to reduce the occurrences of restenosis and thrombosis.OCT(Optical Coherence Tomography) is an emerging technology with capability of acquiring high resolution and real-time images. It has been intensively studied in clinics to guide stent implantation during coronary artery surgery. OCT could acquire 2D coronary artery cross-sectional image sequences. Through image processing, the 2D image sequence could be converted into three-dimensional images to disclose spatial relation between the stent and coronary artery. Physicians could rely on the information to evaluate stent status after implantation.In this thesis, we studied algorisms for 3D images reconstructing from 2D cross-sectional coronary images. The images was firstly denoised by self-adapted median filter algorism. According to the characteristics of OCT images, an improved Otsu algorithm was used to segment the coronary vessels.We then applied a new full automatic algorithm for stent detection. All 2D OCT images were converted into polar coordination. Based on the visible stent features shown in the OCT images, with global intensity histogram and intensity contours, we could identify candidate pixels and remove the artifacts to locate the stent on the lower boundary. By merging adjacent candidate pixels to a point on the stent, we could then accurately delineate the stent position. This process does not require a priori knowledge of stent status and the contours of blood vessels. It is immune from interferences induced by artifacts and avoids the accuracy problem of clustering algorithm. The final results show that the proposed method could conduct 3D reconstruction and fusion effectively.