Relation between coronary plaque calcium deposits as described by computed tomography coronary angiography and acute results of stent deployment as assessed by intravascular ultrasound

Mechanical performances of balloon post-dilation for improving stent expansion in calcified coronary artery: Computational and experimental investigations

Stent deployment in a calcified coronary artery is often associated with suboptimal outcomes such as stent underexpansion and malapposition. Post-dilation after stent deployment is commonly used for optimal stent implantation. There is no guideline for choosing the post-dilation balloon diameter and inflation pressure. In this work, ex-vivo/in-silico experiments were performed to investigate the efficacy of post-dilation balloon diameter and inflation pressure in improving the stent expansion in a calcified lesion. Post-dilations with three balloon diameters (3 mm, 3.5 mm, and 4 mm) were performed. For each balloon diameter, three inflation pressures (10 atm, 20 atm, and 30 atm) were sequentially applied. In ex-vivo experiments, optical coherence tomography images were acquired during the stenting procedure, i.e., pre- and post-deployment of 3 mm diameter stent, as well as after each post-dilation. The results from in-silico experiments were compared with ex-vivo experiments in terms of lumen area. In addition, stretch ratio analysis was developed to predict the stent-induced lumen area, along with the strain analysis and the in-silico experiments. Results have shown that target lumen area could be achieved with an oversized nominal balloon diameter of +0.5 mm (i.e., 0.5 mm greater than reference lumen diameter) at an inflation pressure of 20 atm. After each post-dilation, fibrotic tissue demonstrated a larger strain, contributing to improved lumen gain. However, minimal changes were observed in calcification. Moreover, a strong correlation (R² = 0.95) between the stretch ratio of fibrotic tissue and lumen area after each post-dilation was observed. This indicated that the morphology of the fibrotic tissue could be a potential marker to predict the lumen gain. The detailed mechanistic quantifications of a single lesion cannot be generalized to all clinical cases. However, this work could be used to provide a fundamental understanding of the post-dilations, to develop experimental protocols for producing generalized guidelines, and to exploit their potential for optimal pre- and post-stent strategies.

Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery

In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion.

IMPACT OF CALCIUM QUANTIFICATIONS ON STENT EXPANSIONS

Severely calcified plaque is of great concern when planning and implementing a stenting intervention. In this work, computational models were developed to investigate the influence of calcium characteristics on stenting outcomes. The commonly used clinical measurements of calcium (i.e., the arc angle, maximum thickness, length, and volume) were varied to estimate stenting outcomes in terms of lumen gain, stent underexpansion, strut malapposition, and stress or strain distributions of the stenotic lesion. Results have shown that stenting outcomes were most sensitive to the arc angle of the calcium. A thick calcium with a large arc angle resulted in poor stenting outcomes, such as severe stent underexpansion, D-shaped lumen, increased strut malapposition, and large stresses or strains in the plaque. This was attributed to the circumferential stretch of the tissue. Specifically, the non-calcium component was stretched significantly more than the calcium. The circumferential stretch ratios of calcium and non-calcium component were approximately 2.35 and 1.44, respectively, regardless of calcium characteristics. In addition, the peak stress or strain within the artery and non-calcium component of the plaque occurred at the area adjacent to calcium edges (i.e., the interface between the calcium and the non-calcium component) coincident with the location of peak malapposition. It is worth noting that the calcium played a protective role for the artery underneath, which was at the expense of the overstretch and stress concentrations in the other portion of the artery.