Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging—they are relatively invisible via angiography—and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R^2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images

Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling

Automatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging—they are relatively invisible via angiography—and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R^2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

Cardiovascular Aspects of Radiolog

Intravascular OCT tissue type imaging by automated optical attenuation analysis

We developed attenuation imaging in OCT for atherosclerotic tissue characterization and validated the method ex and in-vivo. We introduced an en-face map of attenuation in the whole artery for plaque visualization. We quantified the attenuation derived from OCT and derived an index for the plaques. A single centre clinical study (OC3T study) was conducted to validate the index to identify thin cap fibroatheromas. We also demonstrated the utility of the attenuation maps and the index in clinical studies as corresponding well with a visual assessment of LCP in the OCT data by expert readers

Automatic quantification of intravascular optical coherence tomography

As mentioned above, IVOCT, as a novel imaging modality, has played an active role in a wide range of CAD applications, including research and clinical routine. Due to its unparalleled high resolution and the ability to delineate complex vascular structures, IVOCT technology makes many precise measurement and novel applications possible. However, currently, a lot of analyses in IVOCT images are still relying on the manual work which decreases their value. The goal of this thesis is to develop robust and accurate (semi)automated methods that can detect and segment the interesting components in IVOCT pullback runs, such as implanted stent struts and side branches in 3D for accurate measurement, so that the results could contribute to medical research as well as for clinical decision-making. My thesis presents four different automated algorithms to detect metallic stent struts, bioresorbable vascular scaffold struts, side branches and all the common components in IVOCT images. It also presented a semi-automated method to assess the stent support to vessel wall and the stent-jailed side branch access through stent cells in 3-dimentional spaceChina Scholarship Council (CSC)UBL - phd migration 201