Jay D. Humphrey
Department of Biomedical Engineering, Yale University, USA
Mechanics, Mechanobiology, and Mechanical Homeostasis in Arteries
All vascular cells are exquisitely sensitive to changes in their mechanical environment, with responses ranging from rapid changes in ion changes to changes in gene expression that affect long-term adaptations or disease progression. Fundamental to such mechanobiological responses is the concept of homeostasis – that cells attempt, when possible, to maintain particular quantities near target “set-points”. There is, therefore, a pressing need for biomechanical analyses that are founded on the concept of mechanical homeostasis and that model the mechanobiology.
In this talk, we will use examples from arterial mechanics and biology as exemplars of soft tissue mechanics and mechanobiology to motivate and illustrate new multiscale methods for understanding mechanical homeostasis and when it is compromised. Hence, time permitting, we will consider observations from vascular development through vascular disease, with a focus on smooth muscle phenotype and discussions of extensions of basic concepts in mechanics to mechanobiology, namely mechanobiological equilibrium and mechanobiological stability.
J.D. Humphrey is the John C. Malone Professor and Chair of Biomedical Engineering at Yale University. He received the Ph.D. in Engineering Science and Mechanics from The Georgia Institute of Technology and completed a post-doctoral fellowship in Medicine - Cardiovascular at Johns Hopkins University. His research and teaching focuses on vascular mechanics and mechanobiology, with particular interest in pediatric diseases as well as hypertension, aneurysms, vascular aging, and tissue engineering. He has authored a graduate textbook (Cardiovascular Solid Mechanics), an undergraduate textbook (An Introduction to Biomechanics), and a handbook (Style and Ethics of Communication in Science and Engineering), and has published 300+ archival journal papers. He served for 10 years as founding co-editor for the journal Biomechanics and Modeling in Mechanobiology, for 12 years on the World Council for Biomechanics, including as Chair of the Technical Program of the 2014 World Congress in Biomechanics, and served for two years as Chair of the US National Committee on Biomechanics. He is a Fellow of the American Institute of Medical and Biological Engineering, the International Academy of Medical and Biological Engineering, and the American Society of Mechanical Engineers, from which he received the H.R. Lissner Medal. He lives with his wife Rita of 38 years in Branford, CT.
Department of Computer Science, University of Cambridge, UK
Institute of Industrial Science, University of Tokyo, Japan
Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Italy
From a digital twin model to an in silico trial: a perilous journey
Osteoporosis is a metabolic disease whose major symptom is an increased probability to experience bone fractures in association with minor trauma, which are usually referred as fragility fractures. There are two areas of research where in silico medicine technologies could be useful to osteoporosis patients. The first is the use of the so-called Biomechanical Computed Tomography (BCT) as a patient-specific prognostic predictor of the risk of fragility fracture at the hip or at the spine (the two type of fragility fractures that are most debilitating). BCT are biophysical digital twin models, where a properly calibrated Quantitative Computed Tomography is used to generate a patient-specific finite element model of portion of interest of the skeleton; by simulating the loading typically experienced during falls, such model can provide an estimator of the risk of fragility fracture in that patient. The second use is to compose a large cohort of digital twin models, each representing a patient with specific characteristics, and use it to run an in silico trial that predicts how the risk of fragility fracture for the cohort will evolve over time. Such model can then be extended to simulate the effect of a new drug on the progression of the disease, in which case the in silico trial can be used to evaluate the efficacy of the new drug against placebo or against a comparator drug. In this study we describe the challenges we faced in transforming a validated model to predict BCT into a fully-featured phase III in silico trial for bone drugs.
Marco Viceconti is full professor of Computational Biomechanics in the department of Industrial Engineering of the Alma Mater Studiorum – University of Bologna, and Director of the Medical Technology Lab of the Rizzoli Orthopaedic Institute. He is also visiting professor at the University of Sheffield, UK, where he founded and led for seven years the prestigious Insigneo Institute for in silico Medicine. Prof Viceconti is an expert of neuromusculoskeletal biomechanics in general, and in particular in the use of subject-specific modelling to support the medical decision. He is one of 25 members of the World Council of Biomechanics. Prof Viceconti is one of the key figures in the in silico medicine international community: he founded the VPH Institute, an international no-profit organisation that coordinates this research community, and drove the creation of the Avicenna Alliance, which represent the biomedical industry interests in this domain. He is a Fellow of the UK Royal Academy of Engineering. According to SCOPUS he published 354 papers (H-index = 50).
Department of Mechanical Engineering, University of Montpellier, France
Computational Physics for Cardiovascular Functional Imaging and Hemocytometry
Since many cardiovascular diseases are related to the characteristics of blood flow, being able to measure or predict how blood circulates is a prerequisite for improving diagnostic and treatment capabilities. Still, measuring blood flow in vivo is a challenging task and, despite the enormous progress made over the past two decades, available medical imaging techniques remain perfectible. In this presentation, I will show how computational fluid dynamics, and more generally computational physics, can be used to supplement, improve or better understand the results of advanced imaging techniques used to measure hemodynamics in clinical routine. I will discuss some of our recent findings regarding phase-contrast magnetic resonance imaging, color Doppler echocardiography and Coulter-based hematology. More details and examples about the developments made at the University of Montpellier towards a reliable solver for hemodynamics at the microscopic and macroscopic scales can be found in the YALES2BIO solver web page: imag.umontpellier.fr/~yales2bio/.
Franck Nicoud graduated in 1990 from the National School of Engineering ENSEEIHT in Toulouse and was awarded a grant from the French Space Agency to carry out a doctoral thesis on the prediction of heat transfers in solid rocket engines. He became a senior scientist at the European Centre for Research and Advanced Training in Scientific Computing (CERFACS) in 1995, joined the Center for Turbulence Research at Stanford University in 1998 and was appointed Professor at the University of Montpellier in 2001, where he is now heading the Polytech Department of Mechanical Engineering and Interactive Design. He founded the YALES2BIO group at the Alexander Grothendieck Institute in Montpellier in 2011, with the aim to develop computational methods and physical models relevant to macroscopic and microscopic blood flows. Being a scientific advisor at CERFACS, some of his research remains driven by aeronautical applications, including wall modeling for Large Eddy Simulations and low order models for thermoacoustic instabilities. He is co-author of approximately 90 articles in international journals, associate editor of the International Journal for Sprays and Combustion Dynamics and co-owns 3 patents.
University of Pavia, Italy
Towards the clinical use of endovascolar procedures, from structural finite element analysis to deep neural network
Endovascular repair of aortic aneurysm (EVAR) is the primary technique to treat aortic aneurysm in high-risk patients. During the procedure, the preoperative 3D model of the aorta is overlaid onto live fluoroscopy images to obtain the three-dimensional roadmap of catheters because image fusion decreases radiation exposure to both patients and clinicians. However, the structural changes caused by the insertion of stiff guide-wire in the aorta degrade the fusion accuracy and the overall procedure performance. Finite-element analysis (FEA) can be exploited to predict the intraoperative modifications and perform a coherent image fusion but FEA usually requires high computational costs limiting its use for real-time applications such intra-operative support. To deal with this, a fully automatic workflow is proposed: deep learning is employed both to perform an automatic and reproducible segmentation of the aorta from Computed Tomography Images (CTAs) and to predict the aortic deformation occurring during endovascular procedure trained with a surrogate population of patients generated by statistical shape modeling where guide-wire simulation is performed by standard structural FEA.
Ferdinando Auricchio is professor of Solids and Structural Mechanics at the University of Pavia. He received the Euler Medal by ECCOMAS (European Community of Computational Methods in Applied Sciences) in 2016 and he became Fellow Award by IACM (International Association for Computational Mechanics) since 2012. From 2013 to 2019 he served as Vice-President of ECCOMAS. In 2018 he was appointed as a member of the Italian National Academy of Science, known also as Accademia dei XL. Major research interests are the development of numerical schemes (in particular, finite element methods, both for solids and fluids, with a particular attention to innovative materials), the development of simulation tools to support medical decision (in particular, for cardiovascular applications), and more recently everything that is related to additive manufacturing.
Department of Mechanical Engineering, Carnegie Mellon University, USA
Material Transport Simulation in Complex Neurite Networks Using Isogeometric Analysis and Machine Learning Techniques
Neurons exhibit remarkably complex geometry in their neurite networks. So far, how materials are transported in the complex geometry for survival and function of neurons remains an unanswered question. Answering this question is fundamental to understanding the physiology and disease of neurons. Here, we develop an isogeometric analysis (IGA) based platform for material transport simulation in neurite networks. We model the transport process by reaction-diffusion-transport equations and represent geometry of the networks using truncated hierarchical tricubic B-splines (THB-spline3D). We solve the Navier-Stokes equations to obtain the velocity field of material transport in the networks. We then solve the transport equations using the streamline upwind/Petrov-Galerkin (SU/PG) method. Using our IGA solver, we simulate material transport in a number of representative and complex neurite networks. From the simulation we discover several spatial patterns of the transport process. Together, our simulation provides key insights into how material transport in neurite networks is mediated by their complex geometry.
To enable fast prediction of the transport process within complex neurite networks, we develop a Graph Neural Networks (GNN) based model to learn the material transport mechanism from simulation data. In this study, we build the graph representation of the neuron by decomposing the neuron geometry into two basic structures: pipe and bifurcation. Different GNN simulators are designed for these two basic structures to predict the spatiotemporal concentration distribution given input simulation parameters and boundary conditions. In particular, we add the residual term from PDEs to instruct the model to learn the physics behind the simulation data. To recover the neurite network, a GNN-based assembly model is used to combine all the pipes and bifurcations following the graph representation. The loss function of the assembly model is designed to impose consistent concentration results on the interface between pipe and bifurcation. Through machine learning, we can quickly and accurately provide a prediction of material transport given a new complex neuron tree.
Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Biomedical Engineering. She received her B.Eng. in Automotive Engineering, and M.Eng. in Engineering Mechanics from Tsinghua University, China; and M.Eng. in Aerospace Engineering and Engineering Mechanics and Ph.D. in Computational Engineering and Sciences from Institute for Computational Engineering and Sciences (now Oden Institute), The University of Texas at Austin. She joined CMU in 2007 as an assistant professor, and then was promoted to an associate professor in 2012 and a full professor in 2016. Her research interests include image processing, computational geometry, finite element method, isogeometric analysis, data-driven simulation and their applications in computational biomedicine, materials science and engineering. Zhang has co-authored over 190 publications in peer-reviewed journals and conference proceedings and received several Best Paper Awards. She published a book entitled “Geometric Modeling and Mesh Generation from Scanned Images” with CRC Press, Taylor & Francis Group in 2016. Zhang is the recipient of Simons Visiting Professorship from Mathematisches Forschungsinstitut Oberwolfach of Germany, US Presidential Early Career Award for Scientists and Engineers, NSF CAREER Award, Office of Naval Research Young Investigator Award, and USACM Gallagher Young Investigator Award. At CMU, she received David P. Casasent Outstanding Research Award, George Tallman Ladd and Florence Barrett Ladd Professorship, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering, Donald L. & Rhonda Struminger Faculty Fellow, and George Tallman Ladd Research Award. She is a Fellow of ASME, AIMBE, USACM and ELATE at Drexel.
Institute of Fluid Science, Tohoku Univesity, Japan
Development of PVA-H 3D printer for mimicking an artery
An arterial model is widely used for medical training by clinicians, or preclinical mechanical testing of medical devices including the validation to CFD. The geometry and the mechanical characteristics of model are highly important because the characteristics strongly affect the training and testing.
Last decades, the model studies have been progressed from the viewpoint of mechanical characteristics and the applications of training and testing. The mechanical characteristics of the model are related to the materials of model, and so a hydrogel, especially, Poly vinyl hydrogel (PVA-H) has been attractive in the field of endovascular treatment using catheters because of its surface friction aspects. Moreover, the flow speed changes in the model due to its higher compliance is larger than a rigid-like model.
The next generation of the hydrogel-based model is the improvement of geometrical characteristics by development of 3D printing using PVA-H. We developed a PVA-H 3D printer based on the Fused Deposition Modeling (FDM). In this study, the flow changes in the hydrogel model and the development of 3D printer are focused.
Makoto OHTA is the professor at Institute of Fluid Science, Tohoku University, Japan. He received the Ph.D. in Engineering from Kyoto University, 2001. He worked for the section of Neuroradiology, Geneva University Hospital during 2001-2004 as a postdoctoral fellowship. He moved to Institute of Fluid Science, Tohoku University as an associate professor on 2005 and became a full professor on 2017. He is a fellow of Japan Society of Mechanical Engineers, and He received Awards for Science and Technology (Research Category), The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology on 2019. Also, he received Fluids Science Research Award by Fluid Science Foundation on 2019. He is a convener of ISO/TC150/WG14, Implant for surgery, Models of tissues for mechanical testing of implants.
MOX, Department of Mathematics, Politecnico di Milano
Mesoscale models for microcirculation to study vascular alterations and treatments
The microcirculation exemplifies the mesoscale in physiological systems, bridging larger and smaller scale phenomena. Multiscale mathematical models represent a valuable tool to investigate and understand such phenomena, where a brute force computational approach is not viable yet. For microcirculation accurate models must comprise both interstitial and vascular compartments, along with their complex morphology. Transport phenomena across the vascular wall couple these two main compartments. In addition, several nonlinear effects are required to properly model microvascular flow.
We discuss a sophisticated mathematical model that describes microcirculation form a mesoscale standpoint and we present its applications to study microvascular alterations and treatments. For example, numerical simulations allow us to study how the tissue micro-environment is affected by the microvascular/interstitial coupling. More in general, by means of a global sensitivity analysis approach, we investigate what are the most relevant factors affecting the microvascular environment and how they interact. Transport phenomena are also embedded in the model, to address drug and nanoparticle delivery. Finally we illustrate how the model can describe the impact of different scenarios such as uremic alterations and radiotherapy induced vascular damage on the microvascular environment.
Paolo Zunino is Professor of Numerical analysis at the Laboratory of Modeling and Scientific Computing of Politecnico di Milano. He received his Master Degree in Aerospace Engineering in 1999 from Politecnico di Milano and his Ph.D. in Applied Mathematics in 2002 from EPFL. His research activity addresses the development of mathematical models and numerical approximation methods with application to engineering and life sciences. In particular, he is active in the area of computational fluid dynamics with the study of finite element methods for flow and mass transport. His current research projects span from the investigation of theoretical aspects of continuum mechanics, to the application of numerical simulations in support of engineering and life sciences. As an example, he has focused on biochemical transport in the cardiovascular system and controlled drug release. More recently he became active in the development of tissue perfusion models, with particular focus on the study of flow and transport in the tumor microenvironment. He is co-author of approximately 100 papers in international peer-reviewed journals several conference proceedings, book chapters, a textbook published by Springer-Italia. On 2002 he was awarded with the SIAM Outstanding Paper Prize.