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Background: This study seeks to quantify the effects of upper body veno-arterial extracorporeal membrane oxygenation (VA ECMO) on the anatomical distribution of oxygen delivery in the setting of hypoxic respiratory failure and provide new insights that will guide clinical use of this support strategy to bridge patients to lung transplant., Methods: Employing a patient-specific vascular geometry and a quantitative model of oxygen transport, computational simulations were performed to determine hemodynamics and oxygen delivery in the ascending and descending aorta, left and right coronary arteries, and great vessels during upper body VA ECMO support. Oxygen content in ECMO circuit blood flow was varied while considering different degrees of lung failure severity. Using lumped parameter models to dynamically apply perfusion boundary conditions, hemodynamic parameters and oxygen content were analyzed to assess the effect of ECMO supply titration., Results: The results emphasize the importance of anatomical distribution for tissue oxygen delivery in severe lung failure, with ECMO-derived flow primarily augmenting oxygen content in specific vascular beds. They also demonstrate that although cannulating the subclavian artery can enhance cerebral oxygen delivery, its ability to ensure sufficient oxygen delivery to the coronary circulation seems to be comparatively restricted., Conclusions: The oxygen delivery to a specific vascular area is primarily determined by the oxygen content in the source of perfusion. Caution is advised with upper body VA ECMO for patients with hypoxic respiratory failure and right ventricle dysfunction, due to potential coronary ischemia. Management of these patients is challenging due to disease progression and organ availability uncertainties., Competing Interests: Declaration of competing interest Hamed Moradi denies any competing interests. Raghu Seethala denies any competing interests. Elazer Edelman is the primary investigator of an educational grant to the Massachusetts Institute of Technology (MIT) from Abiomed, Inc. that investigates use of mechanical circulatory support devices. He has developed intellectual property that is licensed to Abiomed, Inc. through MIT. This work is in the related field of mechanical support but is not directly related to the submitted work. Farhad Nezami denies any competing interests. Steven Keller is a co-inventor of intellectual property with Dr. Edelman that is licensed to Abiomed, Inc. through MIT. This work is not directly related to the submitted work., (Copyright © 2024. Published by Elsevier Ltd.)

Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vessel wall. Intravascular imaging, in contrast, can spatially resolve atherosclerotic in cross sectional slices, but is limited in capturing 3D relationships between each slice. Co-registering CCTA and intravascular images enables a variety of clinical research applications but is time consuming and user-dependent. This is due to intravascular images suffering from non-rigid distortions arising from irregularities in the imaging catheter path. To address these issues, we present a morphology-based framework for the rigid and non-rigid matching of intravascular images to CCTA images. To do this, we find the optimal virtual catheter path that samples the coronary artery in CCTA image space to recapitulate the coronary artery morphology observed in the intravascular image. We validate our framework on a multi-center cohort of 40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our registration approach significantly outperforms other approaches for bifurcation alignment. By providing a differentiable framework for multi-modal vascular co-registration, our framework reduces the manual effort required to conduct large-scale multi-modal clinical studies and enables the development of machine learning-based co-registration approaches.

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study's major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease., Competing Interests: Declarations We declare there are no conflicts of interest.

Characterizing coronary calcified plaque (CCP) provides essential insight into diagnosis and treatment of atherosclerosis. Intravascular optical coherence tomography (OCT) offers significant advantages for detecting CCP and even automated segmentation with recent advances in deep learning techniques. Most of current methods have achieved promising results by adopting existing convolution neural networks (CNNs) in computer vision domain. However, their performance can be detrimentally affected by unseen plaque patterns and artifacts due to inherent limitation of CNNs in contextual reasoning. To overcome this obstacle, we proposed a Transformer-based pyramid network called AFS-TPNet for robust, end-to-end segmentation of CCP from OCT images. Its encoder is built upon CSWin Transformer architecture, allowing for better perceptual understanding of calcified arteries at a higher semantic level. Specifically, an augmented feature split (AFS) module and residual convolutional position encoding (RCPE) mechanism are designed to effectively enhance the capability of Transformer in capturing both fine-grained features and global contexts. Extensive experiments showed that AFS-TPNet trained using Lovasz Loss achieved superior performance in segmentation CCP under various contexts, surpassing prior state-of-the-art CNN and Transformer architectures by more than 6.58% intersection over union (IoU) score. The application of this promising method to extract CCP features is expected to enhance clinical intervention and translational research using OCT., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Elazer R. Edelman reports financial support was provided by National Institutes of Health. Elazer R. Edelman reports financial support was provided by Shockwave Medical Inc., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Endothelial cells (ECs) are capable of quickly responding in a coordinated manner to a wide array of stresses to maintain vascular homeostasis. Loss of EC cellular adaptation may be a potential marker for cardiovascular disease and a predictor of poor response to endovascular pharmacological interventions such as drug-eluting stents. Here, we report single-cell transcriptional profiling of ECs exposed to multiple stimulus classes to evaluate EC adaptation., Methods: Human aortic ECs were costimulated with both pathophysiological flows mimicking shear stress levels found in the human aorta (laminar and turbulent, ranging from 2.5 to 30 dynes/cm 2 ) and clinically relevant antiproliferative drugs, namely paclitaxel and rapamycin. EC state in response to these stimuli was defined using single-cell RNA sequencing., Results: We identified differentially expressed genes and inferred the TF (transcription factor) landscape modulated by flow shear stress using single-cell RNA sequencing. These flow-sensitive markers differentiated previously identified spatially distinct subpopulations of ECs in the murine aorta. Moreover, distinct transcriptional modules defined flow- and drug-responsive EC adaptation singly and in combination. Flow shear stress was the dominant driver of EC state, altering their response to pharmacological therapies., Conclusions: We showed that flow shear stress modulates the cellular capacity of ECs to respond to paclitaxel and rapamycin administration, suggesting that while responding to different flow patterns, ECs experience an impairment in their transcriptional adaptation to other stimuli., Competing Interests: Disclosures A.S. Kalluri reports compensation for consulting from nference, inc. A.K. Shalek reports compensation for consulting and scientific advisory board membership from Merck, Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Ochre Bio, and Dahlia Biosciences. The other authors report no conflicts.

Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the prevalence of AS is projected to rise, resulting in a progressively significant healthcare and economic burden. While surgical aortic valve replacement (SAVR) has been the gold standard approach, the less invasive transcatheter aortic valve replacement (TAVR) is poised to become the dominant method for high- and medium-risk interventions. Computational simulations using patient-specific models, have opened new research avenues for optimizing emerging devices and predicting clinical outcomes. The traditional techniques of generating digital replicas of patients' aortic root, native valve, and calcification are time-consuming and labor-intensive processes requiring specialized tools and expertise in anatomy. Alternatively, deep learning models, such as the U-Net architecture, have emerged as reliable and fully automated methods for medical image segmentation. Two-dimensional U-Nets have been shown to produce comparable or more accurate results than trained clinicians' manual segmentation while significantly reducing computational costs. In this study, we have developed a fully automatic AI tool capable of reconstructing the digital twin geometry and analyzing the calcification distribution on the aortic valve. The developed automatic segmentation package enables the modeling of patient-specific anatomies, which can then be used to simulate virtual interventional procedures, optimize emerging prosthetic devices, and predict clinical outcomes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Farhad Rikhtegar Nezami and Elazer R. Edelman report financial support was provided by National Institute of Health (1R01HL161069). Ross Straughan reports financial support was provided by Zeno Karl Schindler Foundation. Ross Straughan, Karim Kadry, Elazer Edelman, Farhad Rikhtegar Nezami have a patent #A Fully Automated Platform to Enable 3-Dimensional Structural Simulations of Coronary Arteries using Intravascular Images pending to Mass General Brigham. Elazer R. Edelman reports funding grants from: AbioMed Inc, Johnson and Johnson, and Shockwave Medical Inc. Elazer R. Edelman reports consulting or advisory relationships with BEV, Amide, Expansion, Synlife, 4 Catalyzer, Cascade, Enright, Colossal, Dyad, Exogenesis, Marusan Pharm Biotech, Peregrine, Cordio, Restore, RECMA, and Tekla. Elazer R. Edelman reports board membership and employment at Autus, BioDevek, CBSET, and C C Labs. Elazer R. Edelman reports employment at: Panther. Elazer R. Edelman reports board membership at: Autus, Buterfy, Luseed, and Xenter. Sahil A. Parikh reports board membership and funding grants from Abbot Vascular and Boston Scientific. Sahil A. Parikh reports funding grants from Shockwave Medical, Surmodics, TriReme Medical, Veryan Medical, Acotec, Concept Medical, and Reflow Medical. Sahil A. Parikh reports board membership at Medtronic, Cordis, and Philips. Sahil A. Parikh reports consulting or advisory relationships with nari, Penumbra, Canon, and Terumo. Sahil A. Parikh reports equity or stocks in eFemoral, Advanced Nanotherapies, and Encompass Vascular., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Asheghan, Javadikasgari, Attary, Rouhollahi, Straughan, Willi, Awal, Sabe, de la Cruz and Nezami.)

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors., Competing Interests: Conflict of interestThe authors declare no competing interests., (© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence., (© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Purpose: Modern medical imaging enables clinicians to effectively diagnose, monitor, and treat diseases. However, clinical decision-making often relies on combined evaluation of either longitudinal or disparate image sets, necessitating coregistration of multiple acquisitions. Promising coregistration techniques have been proposed; however, available methods predominantly rely on time-consuming manual alignments or nontrivial feature extraction with limited clinical applicability. Addressing these issues, we present a fully automated, robust, nonrigid registration method, allowing for coregistering of multimodal tomographic vascular image datasets using luminal annotation as the sole alignment feature. Approach: Registration is carried out by the use of the registration metrics defined exclusively for lumens shapes. The framework is primarily broken down into two sequential parts: longitudinal and rotational registration. Both techniques are inherently nonrigid in nature to compensate for motion and acquisition artifacts in tomographic images. Results: Performance was evaluated across multimodal intravascular datasets, as well as in longitudinal cases assessing pre-/postinterventional coronary images. Low registration error in both datasets highlights method utility, with longitudinal registration errors-evaluated throughout the paired tomographic sequences-of 0.29 ± 0.14    mm ( < 2 longitudinal image frames) and 0.18 ± 0.16    mm ( < 1 frame) for multimodal and interventional datasets, respectively. Angular registration for the interventional dataset rendered errors of 7.7 ° ± 6.7 ° , and 29.1 ° ± 23.2 ° for the multimodal set. Conclusions: Satisfactory results across datasets, along with additional attributes such as the ability to avoid longitudinal over-fitting and correct nonlinear catheter rotation during nonrigid rotational registration, highlight the potential wide-ranging applicability of our presented coregistration method., (© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Atherosclerosis is a complex disease altering vasculature morphology, and subsequently flow, with progressive plaque formation, mural disruption, and lumen occlusion. Determination of clinically-relevant plaque components-particularly calcium, lipid, and fibrous tissue-has driven automated image-based tissue characterization. Atherosclerotic tissue of mixed composition type arises when these principal components interdigitate and combine during the course of progressive atherosclerosis. Nevertheless, such mixed plaque is treated non-uniformly, and often neglected, as a distinct class in image analysis. We therefore quantitatively investigate frameworks to characterize mixed and other plaque tissue types, and examine their implications. Convolutional neural networks operated on labeled intravascular optical coherence tomography images using various characterization frameworks. The treatment of mixed plaque by image-based classifiers influenced the accuracy and homogeneity of the segmented classes. Excluding mixed plaque as a class on to itself necessarily assigns heterogeneous lesion subcomponents to one of the three homogeneous subtypes; when included, 61.7% of mixed tissue is labeled as calcium, reducing specificity in homogeneous calcium detection by 34.8%. Segmenting mixed plaque as distinct from homogeneous, non-mixed tissue improves lesion classification. This can be achieved either on the basis of homogeneous tissue classifier prediction uncertainty (77.8% overall accuracy) or by training classifiers to identify mixed plaque as a discrete tissue class (82.9% overall accuracy). Alternatively, mixed plaque can be grouped with one of the homogeneous classes, yielding a single histologically diverse class that helps preserve the homogeneity of the others. Ultimately, the best approach depends upon the alignment of histological and functional distinctions. While no vascular lesion characterization framework or method is universally optimal or appropriate, context should remain central in selecting tissue characterization techniques., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Extracorporeal membrane oxygenation (ECMO) is a vital mechanical circulatory support modality capable of restoring perfusion for the patient in circulatory failure. Despite increasing adoption of ECMO, there is incomplete understanding of its effects on systemic hemodynamics and how the vasculature responds to varying levels of continuous retrograde perfusion. To gain further insight into the complex ECMO:failing heart circulation, computational fluid dynamics simulations focused on perfusion distribution and hemodynamic flow patterns were conducted using a patient-derived aorta geometry. Three case scenarios were simulated: (1) healthy control; (2) 90% ECMO-derived perfusion to model profound heart failure; and, (3) 50% ECMO-derived perfusion to model the recovering heart. Fluid-structure interface simulations were performed to quantify systemic pressure and vascular deformation throughout the aorta over the cardiac cycle. ECMO support alters pressure distribution while decreasing shear stress. Insights derived from computational modeling may lead to better understanding of ECMO support and improved patient outcomes., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Extracorporeal membrane oxygenation (ECMO) via femoral cannulation is a vital intervention capable of rapidly restoring perfusion for patients in shock. Despite increasing use to provide circulatory support, its hemodynamic effects are poorly understood and the impact of patient-specific anatomical variation on perfusion is unknown. This study investigates the complex failing heart-mechanical circulatory support circulation and analyzes the effect of patient-specific vascular anatomical variations on hemodynamics and end-organ perfusion., Methods: Patient-specific vascular geometries were constructed from segmenting clinical computerized tomography angiography images and quantitatively compared using tortuosity, curvature, torsion, and lumen diameter. Computational fluid dynamic simulations were performed on a subset of geometries selected to represent a range of anatomical variation. Heart failure severity was modeled by varying the relative fraction of total flow provided by the heart and the extracorporeal circuit. A 3-element lumped parameter model was applied to accurately and dynamically model distal perfusion boundary conditions. Hemodynamic parameters and end-organ perfusion were analyzed and compared to assess the effect of anatomical variation., Results: Pulsatile antegrade cardiac perfusion and ECMO retrograde perfusion collide in the aorta to form a dynamic watershed region. The size, position, and variation of this region over the cardiac cycle is substantially altered by patient anatomical region. Increased vascular tortuosity reduces the proximal extent of flow from circulatory support and decreases the size of the watershed region., Conclusions: Patient vascular anatomy is a key determinant of the ECMO-failing heart circulation that alters the location and extent of the watershed region and affects the tissues at risk for differential hypoxia and circuit-derived thromboemboli for a given level of support., (Copyright © 2021. Published by Elsevier Ltd.)

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., (© 2021. The Author(s).)

The introduction of Bioresorbable Vascular Scaffolds (BVS) has revolutionized the treatment of atherosclerosis. InSilc is an in silico clinical trial (ISCT) platform in a Cloud-based environment used for the design, development and evaluation of BVS. Advanced multi-disciplinary and multiscale models are integrated in the platform towards predicting the short/acute and medium/long term scaffold performance. In this study, InSilc platform is employed in a use case scenario and demonstrates how the whole in silico pipeline allows the interpretation of the effect of the arterial anatomy configuration on stent implantation.

Recent concern for local drug delivery and withdrawal of the first Food and Drug Administration-approved bioresorbable scaffold emphasizes the need to optimize the relationships between stent design and drug release with imposed arterial injury and observed pharmacodynamics. In this study, we examine the hypothesis that vascular injury is predictable from stent design and that the expanding force of stent deployment results in increased circumferential stress in the arterial tissue, which may explain acute injury poststent deployment. Using both numerical simulations and ex vivo experiments on three different stent designs (slotted tube, corrugated ring, and delta wing), arterial injury due to device deployment was examined. Furthermore, using numerical simulations, the consequence of changing stent strut radial thickness on arterial wall shear stress and arterial circumferential stress distributions was examined. Regions with predicted arterial circumferential stress exceeding a threshold of 49.5 kPa compared favorably with observed ex vivo endothelial denudation for the three considered stent designs. In addition, increasing strut thickness was predicted to result in more areas of denudation and larger areas exposed to low wall shear stress. We conclude that the acute arterial injury, observed immediately following stent expansion, is caused by high circumferential hoop stresses in the interstrut region, and denuded area profiles are dependent on unit cell geometric features. Such findings when coupled with where drugs move might explain the drug-device interactions., Competing Interests: CR is employed by the company HeartFlow Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Conway, Nezami, Rogers, Groothuis, Squire and Edelman.)

The pathophysiology of atherosclerotic lesions, including plaque rupture triggered by mechanical failure of the vessel wall, depends directly on the plaque morphology-modulated mechanical response. The complex interplay between lesion morphology and structural behaviour can be studied with high-fidelity computational modelling. However, construction of three-dimensional (3D) and heterogeneous models is challenging, with most previous work focusing on two-dimensional geometries or on single-material lesion compositions. Addressing these limitations, we here present a semi-automatic computational platform, leveraging clinical optical coherence tomography images to effectively reconstruct a 3D patient-specific multi-material model of atherosclerotic plaques, for which the mechanical response is obtained by structural finite-element simulations. To demonstrate the importance of including multi-material plaque components when recovering the mechanical response, a computational case study was conducted in which systematic variation of the intraplaque lipid and calcium was performed. The study demonstrated that the inclusion of various tissue components greatly affected the lesion mechanical response, illustrating the importance of multi-material formulations. This platform accordingly provides a viable foundation for studying how plaque micro-morphology affects plaque mechanical response, allowing for patient-specific assessments and extension into clinically relevant patient cohorts.

Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist's visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks is competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

The bicuspid aortic valve (BAV) is a common and heterogeneous congenital heart abnormality that is often complicated by aortic stenosis. Although initially developed for tricuspid aortic valves (TAV), transcatheter aortic valve replacement (TAVR) devices are increasingly applied to the treatment of BAV stenosis. It is known that patient-device relationship between TAVR and BAV are not equivalent to those observed in TAV but the nature of these differences are not well understood. We sought to better understand the patient-device relationships between TAVR devices and the two most common morphologies of BAV. We performed finite element simulation of TAVR deployment into three cases of idealized aortic anatomies (TAV, Sievers 0 BAV, Sievers 1 BAV), derived from patient-specific measurements. Valve leaflet von Mises stress at the aortic commissures differed by valve configuration over a ten-fold range (TAV: 0.55 MPa, Sievers 0: 6.64 MPa, and Sievers 1: 4.19 MPa). First principle stress on the aortic wall was greater in Sievers 1 (0.316 MPa) and Sievers 0 BAV (0.137 MPa) compared to TAV (0.056 MPa). TAVR placement in Sievers 1 BAV demonstrated significant device asymmetric alignment, with 1.09 mm of displacement between the center of the device measured at the annulus and at the leaflet free edge. This orifice displacement was marginal in TAV (0.33 mm) and even lower in Sievers 0 BAV (0.23 mm). BAV TAVR, depending on the subtype involved, may encounter disparate combinations of device under expansion and asymmetry compared to TAV deployment. Understanding the impacts of BAV morphology on patient-device relationships can help improve device selection, patient eligibility, and the overall safety of TAVR in BAV., Competing Interests: The authors have declared that no competing interests exist.

Bioresorbable vascular scaffolds were considered the fourth generation of endovascular implants deemed to revolutionize cardiovascular interventions. Yet, unexpected high risk of scaffold thrombosis and post-procedural myocardial infractions quenched the early enthusiasm and highlighted the gap between benchtop predictions and clinical observations. To better understand scaffold behavior in the mechanical environment of vessels, animal, and benchtop tests with multimodal loading environment were conducted using industrial standard scaffolds. Finite element analysis was also performed to study the relationship among structural failure, scaffold design, and load types. We identified that applying the combination of bending, axial compression, and torsion better reflects incidence observed in-vivo, far more than tranditional single mode loads. Predication of fracture locations is also more accurate when at least bending and axial compression are applied during benchtop tests (>60% fractures at connected peak). These structural failures may be initiated by implantation-induced microstructural damages and worsened by cyclic loads from the beating heart. Ignoring the multi-modal loading environment in benchtop fatigue tests and computational platforms can lead to undetected potential design defects, calling for redefining consensus evaluation strategies for scaffold performance. With the robust evaluation strategy presented herein, which exploits the results of in-vivo, in-vitro and in-silico investigations, we may be able to compare alternative designs of prototypes at the early stages of device development and optimize the performance of endovascular implants according to patients-specific vessel dynamics and lesion configurations in the future.

Extracorporeal membrane oxygenation (ECMO) is increasingly deployed to provide percutaneous mechanical circulatory support despite incomplete understanding of its complex interactions with the failing heart and its effects on hemodynamics and perfusion. Using an idealized geometry of the aorta and its major branches and a peripherally inserted return cannula terminating in the iliac artery, computational fluid dynamic simulations were performed to (1) quantify perfusion as function of relative ECMO flow and (2) describe the watershed region produced by the collision of antegrade flow from the heart and retrograde ECMO flow. To simulate varying degrees of cardiac failure, ECMO flow as a fraction of systemic perfusion was evaluated at 100%, 90%, 75%, and 50% of total flow with the remainder supplied by the heart calculated from a patient-derived flow waveform. Dynamic boundary conditions were generated with a three-element lumped parameter model to accurately simulate distal perfusion. In profound failure (ECMO providing 90% or more of flow), the watershed region was positioned in the aortic arch with minimal pulsatility observed in the flow to the visceral organs. Modest increases in cardiac flow advanced the watershed region into the thoracic aorta with arch perfusion entirely supplied by the heart., Competing Interests: Disclosure: The authors have no conflicts of interest to report., (Copyright © ASAIO 2020.)

In the recent years, Bioresorbable Vascular Scaffolds (BVS) for the treatment of atherosclerosis have been introduced. InSilc is a cloud based in silico clinical trial (ISCT) platform for drug-eluting BVS. The platform integrates multidisciplinary and multiscale models predicting the BVS performance. In this study, we present a use case scenario and demonstrate the functioning of the individual modules and of the whole pipeline and the ability to predict BVS short, medium, long-term outcomes.

Aortic dissections are challenging for it remains perplexing to determine when surgical, endovascular, or medical therapies are optimal. We studied the effect of the multilayer flow modulator (MFM) device in patients with different forms of type-B aortic dissections. CT scans were performed pre-, immediately post-MFM implantation, and multiple times within a 24-month follow-up. Three-dimensional reconstructions were created from these scans and the multilayer or single-layer mesh device placed virtually into the true lumen. We observed that MFM device can sufficiently restore flow perfusion, reduce the false lumen, eliminate local flow recirculation, and reduce wall shear stress distribution globally. Single-layer devices can reduce false lumen dimensions; however, they generate local disturbance and recirculation zones in selected areas at specific time points. Moreover, in polar extremes of dissection, the MFM device restored flow to vital organs perfusing vessels independent of effects on luminal patency. Management of aortic dissections should focus on modulation of blood flow, suppression of local recirculation, and restoration of vital organ perfusion rather than primarily restoring vascular lumen morphology. While the latter restores the geometry of the true lumen, only the former restores homeostasis. Graphical abstract.

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 the 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 3D 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 with human annotations: areas differed by just 11 ± 11% (0.93 ± 0.84 mm 2 ), with a coefficient of determination of 0.89. Overlapping and non-overlapping area ratios were 0.91 and 0.18, respectively, with a 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.

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.

Polymeric bioresorbable scaffolds (BRS), at their early stages of invention, were considered as a promising revolution in interventional cardiology. However, they failed dramatically compared to metal stents showing substantially higher incidence of device failure and clinical events, especially thrombosis. One problem is that use of paradigms inherited from metal stents ignores dependency of polymer material properties on working environment and manufacturing/deployment steps. Unlike metals, polymeric material characterization experiments cannot be considered identical under dry and submerged conditions at varying rates of operation. We demonstrated different material behaviors associated with variable testing environment and parameters. We, then, have employed extracted material models, which are verified by computational methods, to assess the performance of a full-scale BRS in different working condition and under varying procedural strategies. Our results confirm the accepted notion that slower rate of crimping and inflation can potentially reduce stress concentrations and thus reduce localized damages. However, we reveal that using a universal set of material properties derived from a benchtop experiment conducted regardless of working environment and procedural variability may lead to a significant error in estimation of stress-induced damages and overestimation of benefits procedural updates might offer. We conclude that, for polymeric devices, microstructural damages and localized loss of structural integrity should complement former macroscopic performance-assessment measures (fracture and recoil). Though, to precisely capture localized stress concentration and microstructural damages, context-related testing environment and clinically-relevant procedural scenarios should be devised in preliminary experiments of polymeric resorbable devices to enhance their efficacy and avoid unpredicted clinical events. STATEMENT OF SIGNIFICANCE: Bioresorbable scaffolds (BRS) with the hope to become the next cardiovascular interventional revolution failed in comparison to metal stents. When BRS were characterized using methods for metal stents, designers were misled to seek problem sources at erroneous timeframe and use inefficient indicators, and thus no signal of concern emerged. We demonstrated fundamental flaws associated with applying a universal set of material properties to study device performances in different phases of manufacturing/implantation, and these may be responsible for failure in predicting performance in first-generation BRS. We introduced new criterion for the assessment of structural integrity and device efficacy in next-generation BRS, and indeed all devices using polymeric materials which evolve with the environment they reside in., (Copyright © 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.)

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.

Nitinol stent oversizing is frequently performed in peripheral arteries to ensure a desirable lumen gain. However, the clinical effect of mis-sizing remains controversial. The goal of this study was to provide a better understanding of the structural and hemodynamic effects of Nitinol stent oversizing. Five patient-specific numerical models of non-calcified popliteal arteries were developed to simulate the deployment of Nitinol stents with oversizing ratios ranging from 1.1 to 1.8. In addition to arterial biomechanics, computational fluid dynamics methods were adopted to simulate the physiological blood flow inside the stented arteries. Results showed that stent oversizing led to a limited increase in the acute lumen gain, albeit at the cost of a significant increase in arterial wall stresses. Furthermore, localized areas affected by low Wall Shear Stress increased with higher oversizing ratios. Stents were also negatively impacted by the procedure as their fatigue safety factors gradually decreased with oversizing. These adverse effects to both the artery walls and stents may create circumstances for restenosis. Although the ideal oversizing ratio is stent-specific, this study showed that Nitinol stent oversizing has a very small impact on the immediate lumen gain, which contradicts the clinical motivations of the procedure.