"Insights from Simulating Gait Dynamics and Disorders"
James H. Clark Professor of Bioengineering, Mechanical Engineering, and Orthopaedic Surgery, Stanford University
Co-Director, Simbios, NIH Center for Biomedical Computation
Project Leader for the OpenSim Software
Principal Investigator, National Center for Simulation in Rehabilitation Research
The outcomes of treatments performed to correct movement abnormalities are variable. This problem exists, in part, because the biomechanical causes of the abnormal movement patterns are unclear, and the effects of common treatments are not understood. I believe that the design of treatments will improve if computer simulations are developed that complement experimental movement analysis and explain the causes of movement abnormalities and predict the functional consequences of interventions. This presentation will describe a range of computer simulations that provide insights into the dynamics of human walking and running and the mechanics of common movement abnormalities.
Suvranu De, ScD
Professor and Head, Department of Mechanical, Aerospace and Nuclear Engineering
Director, Center for Modeling, Simulation and Imaging in Medicine (CeMSIM)
Rensselaer Polytechnic Institute
Surgical procedures and devices are traditionally developed on animal models, through extensive trial and error. Similarly, surgical skills are acquired primarily in the operating room on real patients through graded supervision, while training of aviation pilots is now primarily accomplished on sophisticated flight simulators. It is anticipated that virtual surgery systems, that provide immersive computational environments in which the surgeons can interact with three-dimensional organ models using their sense of vision as well as touch, through haptic interface devices, will transform the field of surgery by facilitating discovery of novel surgical procedures, devices and platforms and allowing surgical training to attain competence in a controlled environment that does not expose actual patients to the bare brunt of their "learning curves". However, developing a virtual surgery system is nontrivial and involves solving or addressing a range of issues including physics-based methods for modeling and simulation in real time; realistic simulation of surgical tool-soft tissue interactions with real-time changes to the model's topology; soft tissue mechanical property measurement under various pathological conditions (diseased and normal); mathematical modeling of soft tissue behavior; real-time and stable haptic feedback; full connection between 3D deformable anatomical models under various pathological conditions to real-time VR visualizations; validation and performance metrics; and open source architecture for collaborative and incremental research efforts. In this talk we will present some of our recent work in the development and clinical validation of virual surgery systems.
Supported by: NIH R01EB010037, R01EB009362, R01EB005807, R01EB014305
Andrew D. McCulloch, Ph.D.
"Multi-Scale Modeling of the Failing Heart: From Mouse to Man"
The National Biomedical Computation Resource is developing tools for multi-scale modeling of biological systems that integrate structurally across physical scales of biological organization from molecule to organ system and functionally across interacting biological processes including electrophysiology, mechanics, signaling and metabolism. Here, I illustrate this approach with two examples. The first is a murine model of heart failure that we use in conjunction with a multi-scale model of myocardial mechanics to illustrate how defects at the crossbridge level can affect thin filament activation, twitch kinetics, regional ventricular wall motion and pump function. These studies revealed early dysfunction that suggests a novel mechanism of heart failure onset.
In the second example, I summarize progress on developing patient-specific models of cardiac electromechanics in dyssynchronous heart failure. Cardiac resynchronization therapy can reduce mortality and morbidity and improve outcomes and quality of life in patients with heart failure complicated by electrical dyssynchrony. However, over 30% of candidates for this therapy are considered non-responders. Patient-specific models may help to identify non-responders or improve response rates by optimizing pacing lead placement.
Supported by:NIH grants 5P01HL46345-14, 1R01HL96544-03, 1R01HL105242-01, RO1HL091036-03, 8P41GM103426-19