Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson’s correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts’ annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.

Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque in situ. However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. In this article, we present an image-based tissue characterization method that can be applied to entire acquisition sequences post hoc for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.

Automated analysis of vascular imaging techniques is limited by the inability to precisely determine arterial borders. Intravascular optical coherence tomography (OCT) offers unprecedented detail of artery wall structure and composition, but does not provide consistent visibility of the outer border of the vessel due to limited penetration depth. Existing interpolation and surface fitting methods prove insufficient to accurately fill the gaps between the irregularly-spaced and sometimes unreliably-identified visible segments of the vessel outer border. This paper describes an intuitive, efficient, and flexible new method of three-dimensional surface fitting and smoothing suitable for this task. An anisotropic linear-elastic mesh is fit to irregularly-spaced and uncertain data points corresponding to visible segments of vessel borders, enabling the fully-automated delineation of the entire inner and outer borders of diseased vessels in OCT images for the first time. In a clinical dataset, the proposed smooth surface fitting approach had great agreement when compared to human annotations: areas differed by just 11±11% (0.93±0.84 mm2), with a coefficient of determination of 0.89. Overlapping and non-overlapping area ratios were 0.91 and 0.18, respectively, with sensitivity of 90.8 and specificity of 99.0. This spring mesh method of contour fitting significantly outperformed all alternative surface fitting and interpolation approaches tested. The application of this promising proposed method is expected to enhance clinical intervention and translational research using OCT.

We present a novel and time-efficient method for intracoronary lumen detection, which produces three-dimensional (3-D) coronary arteries using optical coherence tomographic (OCT) images. OCT images are acquired for multiple patients and longitudinal cross-section (LOCS) images are reconstructed using different acquisition angles. The lumen contours for each LOCS image are extracted and translated to 2-D cross-sectional images. Using two angiographic projections, the centerline of the coronary vessel is reconstructed in 3-D, and the detected 2-D contours are transformed to 3-D and placed perpendicular to the centerline. To validate the proposed method, 613 manual annotations from medical experts were used as gold standard. The 2-D detected contours were compared with the annotated contours, and the 3-D reconstructed models produced using the detected contours were compared to the models produced by the annotated contours. Wall shear stress (WSS), as dominant hemodynamics factor, was calculated using computational fluid dynamics and 844 consecutive 2-mm segments of the 3-D models were extracted and compared with each other. High Pearson's correlation coefficients were obtained for the lumen area (r = 0.98) and local WSS (r = 0.97) measurements, while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping and nonoverlapping areas ratio between experts' annotations and presented method was 0.92 and 0.14, respectively. The proposed computer-aided lumen extraction and 3-D vessel reconstruction method is fast, accurate, and likely to assist in a number of research and clinical applications.

Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.

Coronary artery disease (CAD) is a major contributor to death in developed countries and accounts for almost one third of all deaths in individuals over 35 years.¹ CAD is characterized by the development of atherosclerosis and occlusive plaque burden in the coronary artery.^2,3 Arterial stenoses impair distal flow and induce cardiac destruction with myocardial infarction.⁴ Minimally invasive procedures, such as metallic stent and polymeric scaffold angioplasty, address specific lesions⁵ without resorting to open-heart surgery, minimizing procedural risk and speeding recovery times.^5,6 Endovascular implants are inserted with a catheter through one of the major arteries like the femoral artery and are usually balloon-expanded to hold open a stenosed region, enabling previously constricted blood to pass freely.⁷