Constraining OCT with Knowledge of Device Design Enables High Accuracy Hemodynamic Assessment of Endovascular Implants

Experimental validation and clinical feasibility of 3D reconstruction of coronary artery bifurcation stents using intravascular ultrasound

The structural morphology of coronary stents and the local hemodynamic environment following stent deployment in coronary arteries are crucial determinants of procedural success and subsequent clinical outcomes. High-resolution intracoronary imaging has the potential to facilitate geometrically accurate three-dimensional (3D) reconstruction of coronary stents. This work presents an innovative algorithm for the 3D reconstruction of coronary artery stents, leveraging intravascular ultrasound (IVUS) and angiography. The accuracy and reproducibility of our method were tested in stented patient-specific silicone models, with micro-computed tomography serving as a reference standard. We also evaluated the clinical feasibility and ability to perform computational fluid dynamics (CFD) studies in a clinically stented coronary bifurcation. Our experimental and clinical studies demonstrated that our proposed algorithm could reproduce the complex 3D stent configuration with a high degree of precision and reproducibility. Moreover, the algorithm was proved clinically feasible in cases with stents deployed in a diseased coronary artery bifurcation, enabling CFD studies to assess the hemodynamic environment. In combination with patient-specific CFD studies, our method can be applied to stenting optimization, training in stenting techniques, and advancements in stent research and development.

Angiography and optical coherence tomography derived shear stress: are they equivalent in my opinion?

Advances in image reconstruction using either single or multimodality imaging data provide increasingly accurate three-dimensional (3D) patient's arterial models for shear stress evaluation using computational fluid dynamics (CFD). We aim to evaluate the impacts on endothelial shear stress (ESS) derived from a simple image reconstruction using 3D-quantitative coronary angiography (3D-QCA) versus a multimodality reconstruction method using optical coherence tomography (OCT) in patients' vessels treated with bioresorbable scaffolds. Seven vessels at baseline and five-year follow-up of seven patients from a previous CFD investigation were retrospectively selected for a head-to-head comparison of angiography-derived versus OCT-derived ESS. 3D-QCA significantly underestimated the minimum stent area [MSA] (-2.38mm2) and the stent length (-1.46 mm) compared to OCT-fusion method reconstructions. After carefully co-registering the region of interest for all cases with a sophisticated statistical method, the difference in MSA measurements as well as the inability of angiography to visualise the strut footprint in the lumen surface have translated to higher angiography-derived ESS than OCT-derived ESS (1.76 Pa or 1.52 times for the overlapping segment). The difference in ESS widened with a more restricted region of interest (1.97 Pa or 1.63 times within the scaffold segment). Angiography and OCT offer two distinctive methods of ESS calculation. Angiography-derived ESS tends to overestimate the ESS compared to OCT-derived ESS. Further investigations into ESS analysis resolution play a vital role in adopting OCT-derived ESS.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

Semi-Automatic Reconstruction of Patient-Specific Stented Coronaries based on Data Assimilation and Computer Aided Design

PURPOSE

The interplay between geometry and hemodynamics is a significant factor in the development of cardiovascular diseases. This is particularly true for stented coronary arteries. To elucidate this factor, an accurate patient-specific analysis requires the reconstruction of the geometry following the stent deployment for a computational fluid dynamics (CFD) investigation. The image-based reconstruction is troublesome for the different possible positions of the stent struts in the lumen and the coronary wall. However, the accurate inclusion of the stent footprint in the hemodynamic analysis is critical for detecting abnormal stress conditions and flow disturbances, particularly for thick struts like in bioresorbable scaffolds. Here, we present a novel reconstruction methodology that relies on Data Assimilation and Computer Aided Design.

METHODS

The combination of the geometrical model of the undeployed stent and image-based data assimilated by a variational approach allows the highly automated reconstruction of the skeleton of the stent. A novel approach based on computational mechanics defines the map between the intravascular frame of reference (called L-view) and the 3D geometry retrieved from angiographies. Finally, the volumetric expansion of the stent skeleton needs to be self-intersection free for the successive CFD studies; this is obtained by using implicit representations based on the definition of Nef-polyhedra.

RESULTS

We assessed our approach on a vessel phantom, with less than 10% difference (properly measured) vs. a customized manual (and longer) procedure previously published, yet with a significant higher level of automation and a shorter turnaround time. Computational hemodynamics results were even closer. We tested the approach on two patient-specific cases as well.

CONCLUSIONS

The method presented here has a high level of automation and excellent accuracy performances, so it can be used for larger studies involving patient-specific geometries.

Three dimensional reconstruction of coronary artery stents from optical coherence tomography: experimental validation and clinical feasibility

The structural morphology of coronary stents (e.g. stent expansion, lumen scaffolding, strut apposition, tissue protrusion, side branch jailing, strut fracture), and the local hemodynamic environment after stent deployment are key determinants of procedural success and subsequent clinical outcomes. High-resolution intracoronary imaging has the potential to enable the geometrically accurate three-dimensional (3D) reconstruction of coronary stents. The aim of this work was to present a novel algorithm for 3D stent reconstruction of coronary artery stents based on optical coherence tomography (OCT) and angiography, and test experimentally its accuracy, reproducibility, clinical feasibility, and ability to perform computational fluid dynamics (CFD) studies. Our method has the following steps: 3D lumen reconstruction based on OCT and angiography, stent strut segmentation in OCT images, packaging, rotation and straightening of the segmented struts, planar unrolling of the segmented struts, planar stent wireframe reconstruction, rolling back of the planar stent wireframe to the 3D reconstructed lumen, and final stent volume reconstruction. We tested the accuracy and reproducibility of our method in stented patient-specific silicone models using micro-computed tomography (μCT) and stereoscopy as references. The clinical feasibility and CFD studies were performed in clinically stented coronary bifurcations. The experimental and clinical studies showed that our algorithm (1) can reproduce the complex spatial stent configuration with high precision and reproducibility, (2) is feasible in 3D reconstructing stents deployed in bifurcations, and (3) enables CFD studies to assess the local hemodynamic environment within the stent. Notably, the high accuracy of our algorithm was consistent across different stent designs and diameters. Our method coupled with patient-specific CFD studies can lay the ground for optimization of stenting procedures, patient-specific computational stenting simulations, and research and development of new stent scaffolds and stenting techniques.

The Evolution of Data Fusion Methodologies Developed to Reconstruct Coronary Artery Geometry From Intravascular Imaging and Coronary Angiography Data: A Comprehensive Review

Understanding the mechanisms that regulate atherosclerotic plaque formation and evolution is a crucial step for developing treatment strategies that will prevent plaque progression and reduce cardiovascular events. Advances in signal processing and the miniaturization of medical devices have enabled the design of multimodality intravascular imaging catheters that allow complete and detailed assessment of plaque morphology and biology. However, a significant limitation of these novel imaging catheters is that they provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they cannot portray vessel geometry and 3D lesion architecture. To address this limitation computer-based methodologies and user-friendly software have been developed. These are able to off-line process and fuse intravascular imaging data with X-ray or computed tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The aim of this review article is to summarize the evolution in the field of coronary artery modeling; we thus present the first methodologies that were developed to model vessel geometry, highlight the modifications introduced in revised methods to overcome the limitations of the first approaches and discuss the challenges that need to be addressed, so these techniques can have broad application in clinical practice and research.

Establishment of an Automated Algorithm Utilizing Optical Coherence Tomography and Micro-Computed Tomography Imaging to Reconstruct the 3-D Deformed Stent Geometry

Percutaneous coronary intervention (PCI) is the prevalent treatment for coronary artery disease, with hundreds of thousands of stents implanted annually. Computational studies have demonstrated the role of biomechanics in the failure of vascular stents, but clinical studies is this area are limited by a lack of understanding of the deployed stent geometry, which is required to accurately model and predict the stent-induced in vivo biomechanical environment. Herein, we present an automated method to reconstruct the 3-D deployed stent configuration through the fusion of optical coherence tomography (OCT) and micro-computed tomography ( μ CT) imaging data. In an experimental setup, OCT and μ CT data were collected in stents deployed in arterial phantoms ( n=4 ). A constrained iterative deformation process directed by diffeomorphic metric mapping was developed to deform μ CT data of a stent wireframe to the OCT-derived sparse point cloud of the deployed stent. Reconstructions of the deployed stents showed excellent agreement with the ground-truth configurations, with the distance between corresponding points on the reconstructed and ground-truth configurations of [Formula: see text]. Finally, reconstructions required <30 min of computational time. In conclusion, the developed and validated reconstruction algorithm provides a complete spatially resolved reconstruction of a deployed vascular stent from commercially available imaging modalities and has the potential, with further development, to provide more accurate computational models to evaluate the in vivo post-stent mechanical environment, as well as clinical visualization of the 3-D stent geometry immediately following PCI.

Quantification of thrombus formation in malapposed coronary stents deployed in vitro through imaging analysis

Stent thrombosis is a major complication of coronary stent and scaffold intervention. While often unanticipated and lethal, its incidence is low making mechanistic examination difficult through clinical investigation alone. Thus, throughout the technological advancement of these devices, experimental models have been indispensable in furthering our understanding of device safety and efficacy. As we refine model systems to gain deeper insight into adverse events, it is equally important that we continue to refine our measurement methods. We used digital signal processing in an established flow loop model to investigate local flow effects due to geometric stent features and ultimately its relationship to thrombus formation. A new metric of clot distribution on each microCT slice termed normalized clot ratio was defined to quantify this distribution. Three under expanded coronary bare-metal stents were run in a flow loop model to induce clotting. Samples were then scanned in a MicroCT machine and digital signal processing methods applied to analyze geometric stent conformation and spatial clot formation. Results indicated that geometric stent features play a significant role in clotting patterns, specifically at a frequency of 0.6225 Hz corresponding to a geometric distance of 1.606 mm. The magnitude-squared coherence between geometric features and clot distribution was greater than 0.4 in all samples. In stents with poor wall apposition, ranging from 0.27 mm to 0.64 mm maximum malapposition (model of real-world heterogeneity), clots were found to have formed in between stent struts rather than directly adjacent to struts. This early work shows how the combination of tools in the areas of image processing and signal analysis can advance the resolution at which we are able to define thrombotic mechanisms in in vitro models, and ultimately, gain further insight into clinical performance.

Numerical methods for polyline-to-point-cloud registration with applications to patient-specific stent reconstruction

We present novel numerical methods for polyline-to-point-cloud registration and their application to patient-specific modeling of deployed coronary artery stents from image data. Patient-specific coronary stent reconstruction is an important challenge in computational hemodynamics and relevant to the design and improvement of the prostheses. It is an invaluable tool in large-scale clinical trials that computationally investigate the effect of new generations of stents on hemodynamics and eventually tissue remodeling. Given a point cloud of strut positions, which can be extracted from images, our stent reconstruction method aims at finding a geometrical transformation that aligns a model of the undeployed stent to the point cloud. Mathematically, we describe the undeployed stent as a polyline, which is a piecewise linear object defined by its vertices and edges. We formulate the nonlinear registration as an optimization problem whose objective function consists of a similarity measure, quantifying the distance between the polyline and the point cloud, and a regularization functional, penalizing undesired transformations. Using projections of points onto the polyline structure, we derive novel distance measures. Our formulation supports most commonly used transformation models including very flexible nonlinear deformations. We also propose 2 regularization approaches ensuring the smoothness of the estimated nonlinear transformation. We demonstrate the potential of our methods using an academic 2D example and a real-life 3D bioabsorbable stent reconstruction problem. Our results show that the registration problem can be solved to sufficient accuracy within seconds using only a few number of Gauss-Newton iterations.

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 R2 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.

Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses

The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.

Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images

Bioresorbable vascular scaffolds (BVS), the next step in the continuum of minimally invasive vascular interventions present new opportunities for patients and clinicians but challenges as well. As they are comprised of polymeric materials standard imaging is challenging. This is especially problematic as modalities like optical coherence tomography (OCT) become more prevalent in cardiology. OCT, a light-based intracoronary imaging technique, provides cross-sectional images of plaque and luminal morphology. Until recently segmentation of OCT images for BVS struts was performed manually by experts. However, this process is time consuming and not tractable for large amounts of patient data. Several automated methods exist to segment metallic stents, which do not apply to the newer BVS. Given this current limitation coupled with the emerging popularity of the BVS technology, it is crucial to develop an automated methodology to segment BVS struts in OCT images. The objective of this paper is to develop a novel BVS strut detection method in intracoronary OCT images. First, we preprocess the image to remove imaging artifacts. Then, we use a K-means clustering algorithm to automatically segment the image. Finally, we isolate the stent struts from the rest of the image. The accuracy of the proposed method was evaluated using expert estimations on 658 annotated images acquired from 7 patients at the time of coronary arterial interventions. Our proposed methodology has a positive predictive value of 0.93, a Pearson Correlation coefficient of 0.94, and a F1 score of 0.92. The proposed methodology allows for rapid, accurate, and fully automated segmentation of BVS struts in OCT images.

Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.

Computational Model of Drug-Coated Balloon Delivery in a Patient-Specific Arterial Vessel with Heterogeneous Tissue Composition

Balloon angioplasty followed by local delivery of antiproliferative drugs to target tissue is increasingly being considered for the treatment of obstructive arterial disease, and yet there is much to appreciate regarding pharmacokinetics in arteries of non-uniform disease. We developed a computational model capable of simulating drug-coated balloon delivery to arteries of heterogeneous tissue composition comprising healthy tissue, as well as regions of fibrous, fibro-fatty, calcified and necrotic core lesions. Image processing using an unsupervised clustering technique was used to reconstruct an arterial geometry from a single, patient-specific color image obtained from intravascular ultrasound-derived virtual histology. Transport of free drug was modeled using a time-dependent reaction-diffusion model and the bound, immobilized drug using the time-dependent reaction equation. The governing equations representing the transport of free as well as bound drug along with a set of initial settings and boundary conditions were solved numerically using an explicit finite difference scheme that satisfied the Courant-Friedrichs-Lewy stability criterion. Our results support previous findings related to the transport and binding of drug in arteries where tissue retention is strongly dependent on local pharmacologic properties. Additionally, modeling results indicate that non-uniform disease composition leads to heterogeneous arterial drug distribution patterns, although further validation using animal studies is required to fully appreciate pharmacokinetics in disease-laden arteries.

The Systems Biocompatibility of Coronary Stenting

The coronary stent has propelled our understanding of the term "biocompatibility." Stents are expanded at sites of arterial blockage and mechanically reestablish blood flow. This simplicity belies the complex reactions that occur when a stent contacts living substrates. Biocompatible seek to elicit the intended response; stents should perform rather than merely exist. Because performance is assessed in the patient, stent biocompatibility is the multiscale examination of material and cell, and of material, structure, and device in the context of cell, tissue, and organism. This review tracks major biomaterial advances in coronary stent design and discusses biocompatibility clinical performance.