Department: Structural Engineering
Research Institute Affiliation: Center for Extreme Events Research
Faculty Advisor(s): Jiun-Shyan Chen

Primary Student
Name: Qizhi He
Email: q9he@ucsd.edu
Phone: 310-383-8989
Grad Year: 2017

Real-time simulation of skeletal muscles could provide improved patient care and reduced risks by fast prediction of musculoskeletal injuries or disorders due to mechanical loading, diseases and other medical conditions that involve changes of the muscle composition, material properties, and morphology. To this end, an image-based multi-scale computational framework employing Reproducing Kernel Particle Method (RKPM) is proposed for high-fidelity modeling of skeletal muscles. This approach allows direct construction of the model based on pixel data obtained from medical scans such as magnetic resonance images (MRI), from which a level-set segmentation is developed to extract the geometries and compositions of different muscle material components. Under this particle-based framework, the pixel points are utilized directly in the representation of model geometry and the discretization, avoiding the complexity of geometry reconstruction and generation of meshes typically required for the conventional finite element method. The smooth approximation property also allows the representation of material heterogeneity with smooth transitions across material interfaces. For achieving a real-time simulation performance, patient-specific information is pre-computed and pre-stored ?offline?, allowing fast ?online? modeling of large biological systems. The proposed framework is validated against experimental data for force generation in isometric contraction of skeletal muscles.

Industry Application Area(s)
Civil/Structural Engineering | Life Sciences/Medical Devices & Instruments | Materials

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