Aortic stenosis (AS) management is classically guided by symptoms and valvular metrics. However, the natural history of AS is dictated by coupling of the left ventricle, aortic valve, and vascular system. We investigated whether metrics of ventricular and vascular state add to the appreciation of AS state above valve gradient alone. Seventy patients with severe symptomatic AS were prospectively followed from baseline to 30 days after transcatheter aortic valve replacement (TAVR). Quality of life (QOL) was assessed using the Kansas City Cardiomyopathy Questionnaire. Left ventricular stroke work (SWLV) and vascular impedance spectrums were calculated noninvasively using in-house models based on central blood pressure waveforms, along with hemodynamic parameters from echocardiograms. Patients with higher preprocedural SWLV and lower vascular impedance were more likely to experience improved QOL after TAVR. Patients fell into two categories: those who did and those who did not exhibit increase in blood pressure after TAVR. In patients who developed hypertension (19%), vascular impedance increased and SWLV remained unchanged (impedance at zeroth harmonic: Z0, from 3964.4 to 4851.8 dyne·s/cm3, P = 0.039; characteristic impedance: Zc, from 376.2 to 603.2 dyne·s/cm3, P = 0.033). SWLV dropped only in patients who did not develop new hypertension after TAVR (from 1.58 to 1.26 J; P < 0.001). Reduction in valvular pressure gradient after TAVR did not predict change in SWLV (r = 0.213; P = 0.129). Reduction of SWLV after TAVR may be an important metric in management of AS, rather than relying solely on the elimination of transvalvular pressure gradients.

The increasing prevalence of finite element (FE) simulations in the study of atherosclerosis has spawned numerous inverse FE methods for the mechanical characterization of diseased tissue in vivo. Current approaches are however limited to either homogenized or simplified material representations. This paper presents a novel method to account for tissue heterogeneity and material nonlinearity in the recovery of constitutive behavior using imaging data acquired at differing intravascular pressures by incorporating interfaces between various intra-plaque tissue types into the objective function definition. Method verification was performed in silico by recovering assigned material parameters from a pair of vessel geometries: one derived from coronary optical coherence tomography (OCT); one generated from in silico-based simulation. In repeated tests, the method consistently recovered 4 linear elastic (0.1 ± 0.1% error) and 8 nonlinear hyperelastic (3.3 ± 3.0% error) material parameters. Method robustness was also highlighted in noise sensitivity analysis, where linear elastic parameters were recovered with average errors of 1.3 ± 1.6% and 8.3 ± 10.5%, at 5% and 20% noise, respectively. Reproducibility was substantiated through the recovery of 9 material parameters in two more models, with mean errors of 3.0 ± 4.7%. The results highlight the potential of this new approach, enabling high-fidelity material parameter recovery for use in complex cardiovascular computational studies.

Optical coherence tomography (OCT) is a fiber-based intravascular imaging modality that produces high-resolution tomographic images of artery lumen and vessel wall morphology. Manual analysis of the diseased arterial wall is time consuming and sensitive to inter-observer variability; therefore, machine-learning methods have been developed to automatically detect and classify mural composition of atherosclerotic vessels. However, none of the tissue classification methods include in their analysis the outer border of the OCT vessel, they consider the whole arterial wall as pathological, and they do not consider in their analysis the OCT imaging limitations, e.g. shadowed areas. The aim of this study is to present a deep learning method that subdivides the whole arterial wall into six different classes: calcium, lipid tissue, fibrous tissue, mixed tissue, non-pathological tissue or media, and no visible tissue. The method steps include defining wall area (WAR) using previously developed lumen and outer border detection methods, and automatic characterization of the WAR using a convolutional neural network (CNN) algorithm. To validate this approach, 700 images of diseased coronary arteries from 28 patients were manually annotated by two medical experts, while the non-pathological wall and media was automatically detected based on the Euclidian distance of the lumen to the outer border of the WAR. Using the proposed method, an overall classification accuracy 96% is reported, indicating great promise for clinical translation.

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 irregularlyspaced 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 fullyautomated 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 mm 2), with a coefficient of determination of 0.89. Overlapping and nonoverlapping 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

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. 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-