Dynamically scaled phantom phase contrast MRI compared to true-scale computational modeling of coronary artery flow.

PURPOSE: To examine the feasibility of combining computational fluid dynamics (CFD) and dynamically scaled phantom phase-contrast magnetic resonance imaging (PC-MRI) for coronary flow assessment. MATERIALS AND METHODS: Left main coronary bifurcations segmented from computed tomography with bifurcation angles of 33°, 68°, and 117° were scaled-up ∼7× and 3D printed. Steady coronary flow was reproduced in these phantoms using the principle of dynamic similarity to preserve the true-scale Reynolds number, using blood analog fluid and a pump circuit in a 3T MRI scanner. After PC-MRI acquisition, the data were segmented and coregistered to CFD simulations of identical, but true-scale geometries. Velocities at the inlet region were extracted from the PC-MRI to define the CFD inlet boundary condition. RESULTS: The PC-MRI and CFD flow data agreed well, and comparison showed: 1) small velocity magnitude discrepancies (2-8%); 2) with a Spearman's rank correlation ≥0.72; and 3) a velocity vector correlation (including direction) of r(2) ≥ 0.82. The highest agreement was achieved for high velocity regions with discrepancies being located in slow or recirculating zones with low MRI signal-to-noise ratio (SNRv ) in tortuous segments and large bifurcating vessels. CONCLUSION: Characterization of coronary flow using a dynamically scaled PC-MRI phantom flow is feasible and provides higher resolution than current in vivo or true-scale in vitro methods, and may be used to provide boundary conditions for true-scale CFD simulations. J. MAGN. RESON. IMAGING 2016;44:983-992.

Role of secondary flows in coronary artery bifurcations before and after stenting: What is known so far?

Coronary arteries are uniformly exposed to traditional cardiovascular risk factors. However, atherosclerotic lesions occur in preferential regions of the coronary tree, especially in areas with disturbed local blood flow, such as coronary bifurcations. Over the latest years, secondary flows have been linked to the inception and progression of atherosclerosis. Most of these novel findings have been obtained in the field of computational fluid dynamic (CFD) analysis and biomechanics but remain poorly understood by cardiovascular interventionalists, despite the important impact that they may have in clinical practice. We aimed to summarize the current available data regarding the pathophysiological role of secondary flows in coronary artery bifurcation, providing an interpretation of these findings from an interventional perspective.

Towards automated coronary artery segmentation: A systematic review.

BACKGROUND AND OBJECTIVE: Vessel segmentation is the first processing stage of 3D medical images for both clinical and research use. Current segmentation methods are tedious and time consuming, requiring significant manual correction and hence are infeasible to use in large data sets. METHODS: Here, we review and analyse available coronary artery segmentation methods, focusing on fully automated methods capable of handling the rapidly growing medical images available. All manuscripts published since 2010 are systematically reviewed, categorised into different groups based on the approach taken, and characteristics of the different approaches as well as trends over the past decade are explored. RESULTS: The manuscripts were divided intro three broad categories, consisting of region growing, voxelwise prediction and partitioning approaches. The most common approach overall was region growing, particularly using active contour models, however these have had a sharp fall in popularity in recent years with convolutional neural networks becoming significantly more popular. CONCLUSIONS: The systematic review of current coronary artery segmentation methods shows interesting trends, with rising popularity of machine learning methods, a focus on efficient methods, and falling popularity of computationally expensive processing steps such as vesselness and multiplanar reformation.

Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks.

BACKGROUND AND OBJECTIVE: Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings. METHODS: In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set. RESULTS: The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD. CONCLUSIONS: This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration.

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study.

INTRODUCTION: Coronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The 'anatomy of risk' hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD. METHODS AND ANALYSIS: GeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual's CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool. ETHICS AND DISSEMINATION: The study protocol has been approved by the St Vincent's Hospital Human Research Ethics Committee, Sydney-2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee-2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.

Accuracy of vascular tortuosity measures using computational modelling.

Severe coronary tortuosity has previously been linked to low shear stresses at the luminal surface, yet this relationship is not fully understood. Several previous studies considered different tortuosity metrics when exploring its impact of on the wall shear stress (WSS), which has likely contributed to the ambiguous findings in the literature. Here, we aim to analyze different tortuosity metrics to determine a benchmark for the highest correlating metric with low time-averaged WSS (TAWSS). Using Computed Tomography Coronary Angiogram (CTCA) data from 127 patients without coronary artery disease, we applied all previously used tortuosity metrics to the left main coronary artery bifurcation, and to its left anterior descending and left circumflex branches, before modelling their TAWSS using computational fluid dynamics (CFD). The tortuosity measures included tortuosity index, average absolute-curvature, root-mean-squared (RMS) curvature, and average squared-derivative-curvature. Each tortuosity measure was then correlated with the percentage of vessel area that showed a < 0.4 Pa TAWSS, a threshold associated with altered endothelial cell cytoarchitecture and potentially higher disease risk. Our results showed a stronger correlation between curvature-based versus non-curvature-based tortuosity measures and low TAWSS, with the average-absolute-curvature showing the highest coefficient of determination across all left main branches (p < 0.001), followed by the average-squared-derivative-curvature (p = 0.001), and RMS-curvature (p = 0.002). The tortuosity index, the most widely used measure in literature, showed no significant correlation to low TAWSS (p = 0.86). We thus recommend the use of average-absolute-curvature as a tortuosity measure for future studies.

Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge.

Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications.

Topological Optimization of Auxetic Coronary Stents Considering Hemodynamics.

This paper is to design a new type of auxetic metamaterial-inspired structural architectures to innovate coronary stents under hemodynamics via a topological optimization method. The new architectures will low the occurrence of stent thrombosis (ST) and in-stent restenosis (ISR) associated with the mechanical factors and the adverse hemodynamics. A multiscale level-set approach with the numerical homogenization method and computational fluid dynamics is applied to implement auxetic microarchitectures and stenting structure. A homogenized effective modified fluid permeability (MFP) is proposed to efficiently connect design variables with motions of blood flow around the stent, and a Darcy-Stokes system is used to describe the coupling behavior of the stent structure and fluid. The optimization is formulated to include three objectives from different scales: MFP and auxetic property in the microscale and stenting stiffness in the macroscale. The design is numerically validated in the commercial software MATLAB and ANSYS, respectively. The simulation results show that the new design can not only supply desired auxetic behavior to benefit the deliverability and reduce incidence of the mechanical failure but also improve wall shear stress distribution to low the induced adverse hemodynamic changes. Hence, the proposed stenting architectures can help improve safety in stent implantation, to facilitate design of new generation of stents.

A multi-objective optimization of stent geometries.

Stents are scaffolding cardiovascular implants used to restore blood flow in narrowed arteries. However, the presence of the stent alters local blood flow and shear stresses on the surrounding arterial wall, which can cause adverse tissue responses and increase the risk of adverse outcomes. There is a need for optimization of stent designs for hemodynamic performance. We used multi-objective optimization to identify ideal combinations of design variables by assessing potential trade-offs based on common hemodynamic indices associated with clinical risk and mechanical performance of the stents. We studied seven design variables including strut cross-section, strut dimension, strut angle, cell alignment, cell height, connector type and connector arrangement. Optimization objectives were the percentage of vessel area exposed to adversely low time averaged WSS (TAWSS) and adversely high Wall Shear Stress (WSS) assessed using computational fluid dynamics modeling, as well as radial stiffness of the stent using FEA simulation. Two multi-objective optimization algorithms were used and compared to iteratively predict ideal designs. Out of 50 designs, three best designs with respect to each of the three objectives, and two designs in regard to overall performance were identified.

3D Printing for Cardiovascular Applications: From End-to-End Processes to Emerging Developments.

3D printing as a means of fabrication has seen increasing applications in medicine in the last decade, becoming invaluable for cardiovascular applications. This rapidly developing technology has had a significant impact on cardiovascular research, its clinical translation and education. It has expanded our understanding of the cardiovascular system resulting in better devices, tools and consequently improved patient outcomes. This review discusses the latest developments and future directions of generating medical replicas ('phantoms') for use in the cardiovascular field, detailing the end-to-end process from medical imaging to capture structures of interest, to production and use of 3D printed models. We provide comparisons of available imaging modalities and overview of segmentation and post-processing techniques to process images for printing, detailed exploration of latest 3D printing methods and materials, and a comprehensive, up-to-date review of milestone applications and their impact within the cardiovascular domain across research, clinical use and education. We then provide an in-depth exploration of future technologies and innovations around these methods, capturing opportunities and emerging directions across increasingly realistic representations, bioprinting and tissue engineering, and complementary virtual and mixed reality solutions. The next generation of 3D printing techniques allow patient-specific models that are increasingly realistic, replicating properties, anatomy and function.

Design of Self-Expanding Auxetic Stents Using Topology Optimization.

Implanting stents is the most efficient and minimally invasive technique for treating coronary artery diseases, but the risks of stent thrombosis (ST) and in-stent restenosis (IRS) hamper the healing process. There have been a variety of stents in market but dominated by ad hoc design motifs. A systematic design method that can enhance deliverability, safety and efficacy is still in demand. Most existing designs are focused on patient and biological factors, while the mechanical failures related to stenting architectures, e.g., inadequate stent expansion, stent fracture, stent malapposition and foreshortening, are often underestimated. With regard to these issues, the self-expanding (SE) stents may perform better than balloon-expandable (BE) stents, but the SE stents are not popular in clinic practice due to poor deliverability, placement accuracy, and precise match of the stent size and shape to the vessel. This paper addresses the importance between stent structures and clinic outcomes in the treatment of coronary artery disease. First, a concurrent topological optimization method will be developed to systematically find the best material distribution within the design domain. An extended parametric level set method with shell elements is proposed in the topology optimization to ensure the accuracy and efficiency of computations. Second, the auxetic metamaterial with negative Poisson's ratio is introduced into the self-expanding stents. Auxetics can enhance mechanical properties of structures, e.g., fracture toughness, indentation and shear resistance and vibration energy absorption, which will help resolve the drawbacks due to the mechanical failures. Final, the optimized SE stent is numerically validated with the commercial software ANSYS and then prototyped using additive manufacturing techniques. Topological optimization gives a rare opportunity to exploiting the unique advantages of additive manufacturing. Hence, the topologically optimized auxetic architectures will provide a new solution for developing novel stenting structures, especially conductive to self-expanding SE stents. The new design will overcome the limitations of conventional SE stents associated with mechanical structures while maintain their valuable features, to help reduce the occurrence of ST and ISR and benefit the clinic practice in treating coronary heart disease.

Towards validating stent induced micro flow patterns in left main coronary artery bifurcations.

We investigated if blood flow changes induced through the presence of a stent could be detected using in vitro dynamically scaled 4D Phase-Contrast Magnetic Resonance Imaging (PC-MRI). Using idealized and patient-specific left main coronary artery bifurcations, we 3D-printed the dynamically large scaled geometries and incorporated them into a flow circuit for non-invasive acquisition with a higher effective spatial resolution. We tested the effects of using non-Newtonian and Newtonian fluids for the experiment. We also numerically simulated the same geometries in true scale for comparison using computational fluid dynamics (CFD). We found that the experimental setup increased the effective spatial resolution enough to reveal stent induced blood flow changes close to the vessel wall. Non-Newtonian fluid replicated all of the flow field well with a strong agreement with the computed flow field (R2 > 0.9). Fine flow structures were not as prominent for the Newtonian compared to non-Newtonian fluid consideration. In the patient-specific geometry, arterial non-planarity increased the difficulty to capture the near wall slow velocity changes. Findings demonstrate the potential to dynamically scale in vitro 4D MRI flow acquisition for micro blood flow considerations.

Bench testing and coronary artery bifurcations: a consensus document from the European Bifurcation Club.

This is a consensus document from the European Bifurcation Club concerning bench testing in coronary artery bifurcations. It is intended to provide guidelines for bench assessment of stents and other strategies in coronary bifurcation treatment where the United States Food and Drug Administration (FDA) or International Organization for Standardization (ISO) guidelines are limited or absent. These recommendations provide guidelines rather than a step-by-step manual. We provide data on the anatomy of bifurcations and elastic response of coronary arteries to aid model construction. We discuss testing apparatus, bench testing endpoints and bifurcation nomenclature.

A Study of Coronary Bifurcation Shape in a Normal Population.

During percutaneous coronary intervention, stents are placed in narrowings of the arteries to restore normal blood flow. Despite improvements in stent design, deployment techniques and drug-eluting coatings, restenosis and stent thrombosis remain a significant problem. Population stent design based on statistical shape analysis may improve clinical outcomes. Computed tomographic (CT) coronary angiography scans from 211 patients with a zero calcium score, no stenoses and no intermediate artery, were used to create statistical shape models of 446 major coronary artery bifurcations (left main, first diagonal and obtuse marginal and right coronary crux). Coherent point drift was used for registration. Principal component analysis shape scores were tested against clinical risk factors, quantifying the importance of recognised shape features in intervention including size, angles and curvature. Significant differences were found in (1) vessel size and bifurcation angle between the left main and other bifurcations; (2) inlet and curvature angle between the right coronary crux and other bifurcations; and (3) size and bifurcation angle by sex. Hypertension, smoking history and diabetes did not appear to have an association with shape. Physiological diameter laws were compared, with the Huo-Kassab model having the best fit. Bifurcation coronary anatomy can be partitioned into clinically meaningful modes of variation showing significant shape differences. A computational atlas of normal coronary bifurcation shape, where disease is common, may aid in the design of new stents and deployment techniques, by providing data for bench-top testing and computational modelling of blood flow and vessel wall mechanics.

Impact of bifurcation angle and other anatomical characteristics on blood flow - A computational study of non-stented and stented coronary arteries.

The hemodynamic influence of vessel shape such as bifurcation angle is not fully understood with clinical and quantitative observations being equivocal. The aim of this study is to use computational modeling to study the hemodynamic effect of shape characteristics, in particular bifurcation angle (BA), for non-stented and stented coronary arteries. Nine bifurcations with angles of 40°, 60° and 80°, representative of ±1 SD of 101 asymptomatic computed tomography angiogram cases (average age 54±8 years; 57 females), were generated for (1) a non-stented idealized, (2) stented idealized, and (3) non-stented patient-specific geometry. Only the bifurcation angle was changed while the geometries were constant to eliminate flow effects induced by other vessel shape characteristics. The commercially available Biomatrix stent was used as a template and virtually inserted into each branch, simulating the T-stenting technique. Three patient-specific geometries with additional shape variation and ±2 SD BA variation (33°, 42° and 117°) were also computed. Computational fluid dynamics (CFD) analysis was performed for all 12 geometries to simulate physiological conditions, enabling the quantification of the hemodynamic stress distributions, including a threshold analysis of adversely low and high wall shear stress (WSS), low time-averaged WSS (TAWSS), high spatial WSS gradient (WSSG) and high Oscillatory Shear Index (OSI) area. The bifurcation angle had a minor impact on the areas of adverse hemodynamics in the idealized non-stented geometries, which fully disappeared once stented and was not apparent for patient geometries. High WSS regions were located close to the carina around peak-flow, and WSSG increased significantly after stenting for the idealized bifurcations. Additional shape variations affected the hemodynamic profiles, suggesting that BA alone has little effect on a patient׳s hemodynamic profile. Incoming flow angle, diameter and tortuosity appear to have stronger effects. This suggests that other bifurcation shape characteristics and stent placement/strategy may be more important than bifurcation angle in atherosclerotic disease development, progression, and stent outcome.

Hemodynamics in Idealized Stented Coronary Arteries: Important Stent Design Considerations.

Stent induced hemodynamic changes in the coronary arteries are associated with higher risk of adverse clinical outcome. The purpose of this study was to evaluate the impact of stent design on wall shear stress (WSS), time average WSS, and WSS gradient (WSSG), in idealized stent geometries using computational fluid dynamics. Strut spacing, thickness, luminal protrusion, and malapposition were systematically investigated and a comparison made between two commercially available stents (Omega and Biomatrix). Narrower strut spacing led to larger areas of adverse low WSS and high WSSG but these effects were mitigated when strut size was reduced, particularly for WSSG. Local hemodynamics worsened with luminal protrusion of the stent and with stent malapposition, adverse high WSS and WSSG were identified around peak flow and throughout the cardiac cycle respectively. For the Biomatrix stent, the adverse effect of thicker struts was mitigated by greater strut spacing, radial cell offset and flow-aligned struts. In conclusion, adverse hemodynamic effects of specific design features (such as strut size and narrow spacing) can be mitigated when combined with other hemodynamically beneficial design features but increased luminal protrusion can worsen the stent's hemodynamic profile significantly.

Overcoming spatio-temporal limitations using dynamically scaled in vitro PC-MRI - A flow field comparison to true-scale computer simulations of idealized, stented and patient-specific left main bifurcations.

The majority of patients with angina or heart failure have coronary artery disease. Left main bifurcations are particularly susceptible to pathological narrowing. Flow is a major factor of atheroma development, but limitations in imaging technology such as spatio-temporal resolution, signal-to-noise ratio (SNRv), and imaging artefacts prevent in vivo investigations. Computational fluid dynamics (CFD) modelling is a common numerical approach to study flow, but it requires a cautious and rigorous application for meaningful results. Left main bifurcation angles of 40°, 80° and 110° were found to represent the spread of an atlas based 100 computed tomography angiograms. Three left mains with these bifurcation angles were reconstructed with 1) idealized, 2) stented, and 3) patient-specific geometry. These were then approximately 7× scaled-up and 3D printing as large phantoms. Their flow was reproduced using a blood-analogous, dynamically scaled steady flow circuit, enabling in vitro phase-contrast magnetic resonance (PC-MRI) measurements. After threshold segmentation the image data was registered to true-scale CFD of the same coronary geometry using a coherent point drift algorithm, yielding a small covariance error (σ2 <;5.8×10-4). Natural-neighbour interpolation of the CFD data onto the PC-MRI grid enabled direct flow field comparison, showing very good agreement in magnitude (error 2-12%) and directional changes (r2 0.87-0.91), and stent induced flow alternations were measureable for the first time. PC-MRI over-estimated velocities close to the wall, possibly due to partial voluming. Bifurcation shape determined the development of slow flow regions, which created lower SNRv regions and increased discrepancies. These can likely be minimised in future by testing different similarity parameters to reduce acquisition error and improve correlation further. It was demonstrated that in vitro large phantom acquisition correlates to true-scale coronary flow simulations when dynamically scaled, and thus can overcome current PC-MRI's spatio-temporal limitations. This novel method enables experimental assessment of stent induced flow alternations, and in future may elevate CFD coronary flow simulations by providing sophisticated boundary conditions, and enable investigations of stenosis phantoms.

A computational atlas of normal coronary artery anatomy.

AIMS: The aim of this study was to define the shape variations, including diameters and angles, of the major coronary artery bifurcations. METHODS AND RESULTS: Computed tomographic angiograms from 300 adults with a zero calcium score and no stenoses were segmented for centreline and luminal models. A computational atlas was constructed enabling automatic quantification of 3D angles, diameters and lengths of the coronary tree. The diameter (mean±SD) of the left main coronary was 3.5±0.8 mm and the length 10.5±5.3 mm. The left main bifurcation angle (distal angle or angle B) was 89±21° for cases with, and 75±23° for those without an intermediate artery (p<0.001). Analogous measurements of diameter and angle were tabulated for the other major bifurcations (left anterior descending/diagonal, circumflex/obtuse marginal and right coronary crux). Novel 3D angle definitions are proposed and analysed. CONCLUSIONS: A computational atlas of normal coronary artery anatomy provides distributions of diameter, lengths and bifurcation angles as well as more complex shape analysis. These data define normal anatomical variation, facilitating stent design, selection and optimal treatment strategy. These population models are necessary for accurate computational flow dynamics, can be 3D printed for bench testing bifurcation stents and deployment strategies, and can aid in the discussion of different approaches to the treatment of coronary bifurcations.

Construction of a coronary artery atlas from CT angiography.

Describing the detailed statistical anatomy of the coronary artery tree is important for determining the aetiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.