Abstract #2249

Automated pipeline for creating personalized biomechanical knee models and computing personalized cartilage pressures during gait

Kathryn R Marusich¹, Allison Clouthier², Carmichael F Ong³, Anna Bartsch⁴, Feliks Kogan⁵, Garry E Gold⁵, Akshay S Chaudhari⁵, and Anthony A Gatti⁵

¹Department of Mechanical Engineering, Stanford University, Stanford, CA, United States, ²School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada, ³Department of Bioengineering, Stanford University, Stanford, CA, United States, ⁴Department of Orthopedics and Traumatology, University Hospital Basel, Basel, Switzerland, ⁵Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Whole Joint, Cartilage, Cartilage, Biomechanics, Bone, Segmentation, Personalized

Motivation: Automated analysis of MRI and biomechanics data can provide personalized information about cartilage pressures.

Goal(s): Our goal was to develop an automated pipeline to create a personalized biomechanical knee joint model from MRI data, to simulate personalized knee mechanics during gait in comparison to knee mechanics of a generic knee joint geometry.

Approach: Bone and cartilage geometry was automatically segmented from knee MRI scans via deep learning. Gait simulations were performed on musculoskeletal models with personalized and generic knee models.

Results: Personalizing knee joint geometries affected cartilage pressure distributions in the joint but maintained peak cartilage pressures and contact forces.

Impact: Biomechanical models personalized with MRI data enable understanding of how bone geometry influences cartilage pressures during gait, which may lead to better tailoring and evaluation of interventions.

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