A multiscale model for the study of cardiac biomechanics in single-ventricle surgeries: a clinical case

Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions

The overlap between mechanical engineering and medicine is expanding more and more over the years. Engineers are now using their expertise to design and create functional biomaterials and are continually collaborating with physicians to improve patient health. In this review, we explore the state of scientific knowledge in the areas of biomaterials, biomechanics, nanomechanics, and computational fluid dynamics (CFD) in relation to the pharmaceutical and medical industry. Focusing on current research and breakthroughs, we provide an overview of how these fields are being used to create new technologies for medical treatments of human patients. Barriers and constraints in these fields, as well as ways to overcome them, are also described in this review. Finally, the potential for future advances in biomaterials to fundamentally change the current approach to medicine and biology is also discussed.

Mending a broken heart: In vitro, in vivo and in silico models of congenital heart disease

Birth defects contribute to ∼0.3% of global infant mortality in the first month of life, and congenital heart disease (CHD) is the most common birth defect among newborns worldwide. Despite the significant impact on human health, most treatments available for this heterogenous group of disorders are palliative at best. For this reason, the complex process of cardiogenesis, governed by multiple interlinked and dose-dependent pathways, is well investigated. Tissue, animal and, more recently, computerized models of the developing heart have facilitated important discoveries that are helping us to understand the genetic, epigenetic and mechanobiological contributors to CHD aetiology. In this Review, we discuss the strengths and limitations of different models of normal and abnormal cardiogenesis, ranging from single-cell systems and 3D cardiac organoids, to small and large animals and organ-level computational models. These investigative tools have revealed a diversity of pathogenic mechanisms that contribute to CHD, including genetic pathways, epigenetic regulators and shear wall stresses, paving the way for new strategies for screening and non-surgical treatment of CHD. As we discuss in this Review, one of the most-valuable advances in recent years has been the creation of highly personalized platforms with which to study individual diseases in clinically relevant settings.

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

Cardiovascular diseases account for the number one cause of deaths in the world. Part of the reason for such grim statistics is our limited understanding of the underlying mechanisms causing these devastating pathologies, which is made difficult by the invasiveness of the procedures associated with their diagnosis (e.g., inserting catheters into the coronal artery to measure blood flow to the heart). Likewise, it is also difficult to design and test assistive devices without implanting them in vivo. However, with the recent advancements made in biomedical scanning technologies and computer simulations, image-based modeling (IBM) has arisen as the next logical step in the evolution of non-invasive patient-specific cardiovascular medicine. Yet, due to its novelty, it is still relatively unknown outside of the niche field. Therefore, the goal of this manuscript is to review the current state-of-the-art and the limitations of the methods used in this area of research, as well as their applications to personalized cardiovascular investigations and treatments. Specifically, the modeling of three different physics – electrophysiology, biomechanics and hemodynamics – used in the cardiovascular IBM is discussed in the context of the physiology that each one of them describes and the mechanisms of the underlying cardiac diseases that they can provide insight into. Only the “bare-bones” of the modeling approaches are discussed in order to make this introductory material more accessible to an outside observer. Additionally, the imaging methods, the aspects of the unique cardiac anatomy derived from them, and their relation to the modeling algorithms are reviewed. Finally, conclusions are drawn about the future evolution of these methods and their potential toward revolutionizing the non-invasive diagnosis, virtual design of treatments/assistive devices, and increasing our understanding of these lethal cardiovascular diseases.

Effect of Transmural Differences in Excitation-Contraction Delay and Contraction Velocity on Left Ventricle Isovolumic Contraction: A Simulation Study

Recent studies have shown that left ventricle (LV) exhibits considerable transmural differences in active mechanical properties induced by transmural differences in electrical activity, excitation-contraction coupling, and contractile properties of individual myocytes. It was shown that the time between electrical and mechanical activation of myocytes (electromechanical delay: EMD) decreases from subendocardium to subepicardium and, on the contrary, the myocyte shortening velocity (MSV) increases in the same direction. To investigate the physiological importance of this inhomogeneity, we developed a new finite element model of LV incorporating the observed transmural gradients in EMD and MSV. Comparative simulations with the model showed that when EMD or MSV or both were set constant across the LV wall, the LV contractility during isovolumic contraction (IVC) decreased significantly ((dp/dt)max⁡  was reduced by 2 to 38% and IVC was prolonged by 18 to 73%). This was accompanied by an increase of transmural differences in wall stress. These results suggest that the transmural differences in EMD and MSV play an important role in physiological contractility of LV by synchronising the contraction of individual layers of ventricular wall during the systole. Reduction or enhancement of these differences may therefore impair the function of LV and contribute to heart failure.

Virtual medicine: Utilization of the advanced cardiac imaging patient avatar for procedural planning and facilitation

Advances in imaging technology have led to a paradigm shift from planning of cardiovascular procedures and surgeries requiring the actual patient in a "brick and mortar" hospital to utilization of the digitalized patient in the virtual hospital. Cardiovascular computed tomographic angiography (CCTA) and cardiovascular magnetic resonance (CMR) digitalized 3-D patient representation of individual patient anatomy and physiology serves as an avatar allowing for virtual delineation of the most optimal approaches to cardiovascular procedures and surgeries prior to actual hospitalization. Pre-hospitalization reconstruction and analysis of anatomy and pathophysiology previously only accessible during the actual procedure could potentially limit the intrinsic risks related to time in the operating room, cardiac procedural laboratory and overall hospital environment. Although applications are specific to areas of cardiovascular specialty focus, there are unifying themes related to the utilization of technologies. The virtual patient avatar computer can also be used for procedural planning, computational modeling of anatomy, simulation of predicted therapeutic result, printing of 3-D models, and augmentation of real time procedural performance. Examples of the above techniques are at various stages of development for application to the spectrum of cardiovascular disease processes, including percutaneous, surgical and hybrid minimally invasive interventions. A multidisciplinary approach within medicine and engineering is necessary for creation of robust algorithms for maximal utilization of the virtual patient avatar in the digital medical center. Utilization of the virtual advanced cardiac imaging patient avatar will play an important role in the virtual health care system. Although there has been a rapid proliferation of early data, advanced imaging applications require further assessment and validation of accuracy, reproducibility, standardization, safety, efficacy, quality, cost effectiveness, and overall value to medical care.

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Cardiac image modelling: Breadth and depth in heart disease

With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.

The cardiac torsion as a sensitive index of heart pathology: A model study

The torsional behaviour of the heart (i.e. the mutual rotation of the cardiac base and apex) was proved to be sensitive to alterations of some cardiovascular parameters, i.e. preload, afterload and contractility. Moreover, pathologies which affect the fibers architecture and cardiac geometry were proved to alter the cardiac torsion pattern. For these reasons, cardiac torsion represents a sensitive index of ventricular performance. The aim of this work is to provide further insight into physiological and pathological alterations of the cardiac torsion by means of computational analyses, combining a structural model of the two ventricles with simple lumped parameter models of both the systemic and the pulmonary circulations. Starting from diagnostic images, a 3D anatomy based geometry of the two ventricles was reconstructed. The myocytes orientation in the ventricles was assigned according to literature data and the myocardium was modelled as an anisotropic hyperelastic material. Both the active and the passive phases of the cardiac cycle were modelled, and different clinical conditions were simulated. The results in terms of alterations of the cardiac torsion in the presence of pathologies are in agreement with experimental literature data. The use of a computational approach allowed the investigation of the stresses and strains in the ventricular wall as well as of the global hemodynamic parameters in the presence of the considered pathologies. Furthermore, the model outcomes highlight how for specific pathological conditions, an altered torsional pattern of the ventricles can be present, encouraging the use of the ventricular torsion in the clinical practice.