CCES Unicamp

Patient-specific Mechanical Properties Assignment onto CT-based Bone Geometry Models

Nov 18, 2019
ICBT 2019 – Hannover, Germany
Amadeus Alcantara
Bone fracture is a major health concern and a not yet fully understood phenomenon of nature. Due to the accelerated medical advances in the last decades, people’s quality of life is improving and global life expectancy is increasing. However, the older we get the more vulnerable to bone diseases we become. Bone diseases, specially osteoporosis, lead to fragility fractures. Current medical preventive and diagnostic technologies are still unable to effectively anticipate and prevent fragility fractures. On the other hand, computer simulations are powerful tools for fracture analysis. Fracture models that accurately resemble the geometry and mechanical properties of a specific bone can provide quantitative information on the fracture risk of this bone. Patient-specific geometry and mechanical properties of computational bone models are acquired through non- destructive medical imaging techniques. Computed Tomography (CT) provides in vivo bone three-dimensional geometry and bone mineral density through the reconstruction and segmentation of two-dimensional images. Pixels of CT-images are displayed in Hounsfield Units (HU). Bone mineral density and HU values are linearly dependent. Furthermore, bone elastic properties can be calculated from density values for every pixel in the CT. The coupling of 3D bone-geometry meshes with CT-images for the assignment of mechanical properties onto every mesh-element is called material mapping. The material mapping strategy can have a great impact on the assignment of elastic properties. The purpose of this study was to create Matlab algorithms that perform a CT-based material mapping which generates a patient-specific isotropic inhomogeneous linear elastic bone 3D model for structural analysis simulation. Two different material mapping strategies were created. The material mapping strategies presented in this work were investigated and compared with each other and with the public domain software Bonemat.

Related posts

Advancements and Challenges in Cross-Lingual Ontology Alignment

cces cces

Open Data – Challenges in Data Sharing in Multidisciplinary Environments

cces cces

Promoting the use of Open Data in research

cces cces