Osteoporosis is defined as low bone mass, and results in a markedly increased risk of skeletal fractures. Development of new drugs to reduce bone loss or increase bone mass is promising. However, it requires that the individuals at risk can be accurately identified. Current osteoporosis diagnostics is largely based on measurements of bone mineral density (BMD), using dual energy X-ray absorptiometry (DXA) of the hip or the lumbar spine. DXA based BMD is only a moderate predictor of fracture risk.
Our research aims at improving osteoporosis diagnosis and fracture risk assessment. This will be accomplished by combining DXA imaging, a pre-developed shape template, statistical shape modeling, and finite element analysis (FEA).
The project develops a method to estimate the 3D geometry of the hip bone based on a 2D image and a shape template. This method should be able to describe both the external geometry and the internal bone mineral density distribution. Thereafter the shape template and the 2D image of the patient is used together with finite element analyses to assess the bone strength and the individuals' fracture risk.
The project is a collaboration with the Clinical Osteoporosis Research at Orthopedics department at Lund University, Biophysics of Bone and Cartilage Research group at the University of Eastern Finland, Kuopio, Finland.
Funding: The project is funded by the Swedish Research Council
- Grassi, Väänänen, Ristinmaa, Jurvelin and Isaksson: Prediction of femoral strength using 3D finite element models reconstructed from DXA images: validation against experiments: Biomechanics and modeling in Mechanobiology, 2016 (popular summary)
- Grassi, Väänänen, Ristinmaa, Jurvelin and Isaksson: How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements: Journal of Biomechanics, 2016 (popular summary)
- Väänänen, Grassi, Flivik, Jurvelin, Isaksson: Automatically generated 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image: Medical Image Analysis, 2015 (popular summary)
- Grassi and Isaksson. Extracting accurate strain measurements in bone mechanics: a critical review of current methods. Journal of the Mechanical Behavior of Biomedical Materials, 2015 (popular summary)
Osteoarthritis is a musculoskeletal disease that involves progressive degeneration of the articular cartilage in e.g. the hip and knee joints. Current diagnostics is largely based on radiographs and visual examination. Unfortunately, they cannot detect early signs of osteoarthritis. When the disease is detectable, the damage is already irreversible. Hence, novel methods are needed.
Statistical Appearance Models (SAMs) are mathematically trained to describe the variation in shape and density in a population. By integrating functional imaging of the hip bone based on radiographs in 2D with SAMs, patients' hip morphologies can be estimated in 3D.
Our research investigates an improved way of osteoarthritis diagnosis by developing an imaging technique to quantify geometrical shape parameters known to be relevant for the onset of osteoarthritis.
The project is a collaboration with the LOAD network at the Orthopedics department at Lund University, Biophysics of Bone and Cartilage Research group at the University of Eastern Finland, Kuopio, Finland.
Funding: The project is funded by the Crafoord foundation and Reumatikerförbundet, Sweden
Khayyeri, Väänänen, Flivik, Jurvelin, Dahlberg, Isaksson; A Novel Method to Test Association of 3D hip Morphological Parameters with Hip Osteoarthritis. To be presented at 62nd Annual Meeting of the Orthopaedic Research Society; Orlando, Florida, USA, 2016.
Väänänen, Isaksson, Waarsing, Zadpoor, Jurvelin, Weinans: Estimation of rotation in femoral 2D radiographs. Journal of Biomechanics, 2012