Advanced Imaging and In-Vivo Joint Mechanics

We do

Pioneering 3D dynamic X-ray and fluoroscopy methods to capture real joint motion and loading under functional conditions.

Integration with statistical shape modeling and finite element analysis to quantify contact mechanics and implant behavior in total and partial knee arthroplasty.

Application of these technologies to clinical collaborations in orthopaedic surgery, neurology, and rehabilitation medicine.

Journal Publication

[1]Komala, I., Chen, Y.-T., Chen, Y.-C., Yeh, C.-C., & Lu, T.-W.* (2025). A finite element simulation study on the superficial collagen fibril network of knee cartilage under cyclic loading: Effects of fibril crosslink densities. Journal of the Mechanical Behavior of Biomedical Materials, 170, 107100. link

Projects

A 3D Dynamic Fluoroscopy Comparative Analysis of in vivo Kinematics of the Adjacent joints Between Interventions with Customized Insoles with Dynamic Arch Support and rocker shoes in Patients with Talocrural Arthrodesis during Level and Slope walking(NSTC 114-2221-E-039-008)
A 3D Dynamic Fluoroscopy Comparative Analysis of Rigid Body and Articular Surface Kinematics of the Shoulder Complex Following Lower Trapezius Tendon Transfer with Peroneus Longus Tendon Autograft or Achilles Tendon Allografts During Loaded Arm (NSTC 113-2221-E-341-002)
Elevation Individualized Biomechanical Assessment of Ankle Cartilage and Ligaments Post Anterior Talofibular Ligament Tear and Minimally Invasive Reconstruction During Level and Slope Walking and Drop Landing Using Combined 3D Fluoroscopy and Finite Element Analysis (NSTC 113-2221-E-341-001)

A. AI-Enhanced Subject-Specific Bone Modeling

A major focus of our research is the development of artificial intelligence–based methods for constructing highly accurate subject-specific bone models directly from minimally acquired imaging data. Instead of relying on full computed tomography scans—which may be limited by metallic implants, radiation exposure concerns, or insufficient resolution—our laboratory applies deep learning architectures, convolutional neural networks, and statistical shape modeling to reconstruct detailed bone geometries suitable for three-dimensional fluoroscopic analysis. These methods significantly reduce imaging burden while maintaining the fidelity required for precise bone motion tracking. By enabling widespread application of three-dimensional fluoroscopy for in-vivo kinematic measurement, our artificial intelligence pipeline allows researchers and clinicians to assess cartilage loading, joint contact mechanics, and biomechanical risk factors of osteoarthritis with greater accessibility and lower radiation exposure.

B. Finite Element Analysis for Orthopedic Surgery Evaluation

Our laboratory develops and applies subject-specific finite element models to evaluate the biomechanical consequences of major orthopedic procedures, including unicompartmental knee replacement, total knee replacement, robot-assisted surgery, patient-specific instrumentation–guided high tibial osteotomy, and minimally invasive reconstruction of the anterior talofibular ligament in the ankle. These models integrate individual anatomical geometries, ligament material properties derived from stress tests, and in-vivo kinematic data captured through dynamic fluoroscopy. By simulating functional tasks such as level walking, rising and sitting movements, or drop landing, our finite element analyses quantify cartilage contact pressures, ligament tensions, joint stability characteristics, and implant–tissue interactions. This personalized modeling framework enables robust comparisons between surgical techniques, identifies factors contributing to suboptimal outcomes or premature degeneration, and supports evidence-based improvements in surgical planning and rehabilitation strategies.

C. Multi-Scale Knee Biomechanics and Osteoarthritis Mechani

We investigate how injuries such as anterior cruciate ligament deficiency and anterior cruciate ligament reconstruction alter knee joint mechanics and potentially trigger early-onset osteoarthritis. Through a multi-scale finite element approach, we incorporate collagen fiber architecture, cartilage depth-dependent material properties, and individualized ligament behavior into models that reflect both macroscopic joint motion and microscopic tissue responses. Combined with dynamic fluoroscopy and gait analysis, these models allow us to characterize loading rates, abnormal joint kinematics, and altered stress distributions that may accelerate cartilage degeneration. By analyzing healthy individuals, patients with anterior cruciate ligament deficiency, patients with anterior cruciate ligament reconstruction, and individuals who subsequently develop osteoarthritis, we identify mechanical pathways linking instability, altered movement strategies, and tissue deterioration. This work provides an important foundation for preventive strategies, postoperative guidelines, and long-term musculoskeletal health management.

D. Automated Finite Element Modeling for Surgical Planning

To support precision orthopedics and improve long-term surgical outcomes, our team is developing an automated finite element modeling system capable of rapidly generating personalized knee joint models for preoperative planning. This automated pipeline constructs subject-specific bone, cartilage, and ligament representations, integrates implant geometries, and performs finite element simulations to estimate postoperative stresses and forces under realistic movement conditions. By allowing surgeons to evaluate how different prosthetic alignments or implant positions influence ligament loading, cartilage contact behavior, and overall joint mechanics, the system provides a mechanically informed basis for selecting the optimal placement strategy before surgery. This personalized, simulation-driven framework offers a new paradigm for planning unicompartmental knee replacement and total knee replacement, helping reduce implant overloading, improve functional restoration, and potentially extend prosthesis longevity.We investigate how injuries such as anterior cruciate ligament deficiency and anterior cruciate ligament reconstruction alter knee joint mechanics and potentially trigger early-onset osteoarthritis. Through a multi-scale finite element approach, we incorporate collagen fiber architecture, cartilage depth-dependent material properties, and individualized ligament behavior into models that reflect both macroscopic joint motion and microscopic tissue responses. Combined with dynamic fluoroscopy and gait analysis, these models allow us to characterize loading rates, abnormal joint kinematics, and altered stress distributions that may accelerate cartilage degeneration. By analyzing healthy individuals, patients with anterior cruciate ligament deficiency, patients with anterior cruciate ligament reconstruction, and individuals who subsequently develop osteoarthritis, we identify mechanical pathways linking instability, altered movement strategies, and tissue deterioration. This work provides an important foundation for preventive strategies, postoperative guidelines, and long-term musculoskeletal health management.