A quantitative 3D bone imaging technique for the diagnosis of skeletal disorders.
Osteoporosis is a skeletal disorder characterized by compromised bone strength that predisposes individuals to increased bone fragility and consequent risk of fracture. It is the most common metabolic bone disease with a wide and increasing distribution among the elderly. Complications related to osteoporosis, such as vertebral or hip fractures, can create social and economic burdens. Comprehensive visual assessment of bone integrity and quality is essential to understand, diagnose and treat osteoporosis and related disorders. New imaging modalities coupled with novel computational analysis approaches are therefore needed to properly diagnose osteoporosis in its early stages, thereby reducing social and economic costs and preventing patient suffering. The present technology consists of a novel computational method for quantitative detection of bone quality and morphology, thus facilitating diagnosis of various skeletal abnormalities.
- Diagnosis of osteoporosis and other skeletal disorders.
- Research tool for in the area of bone research.
- Early diagnostic of Osteoporosis
- More accurate diagnostic
- Analysis of images based on small field of view
A team of researchers at the Weizmann Institute has developed a unique computational method that enables the evaluation and quantification of osteo-related properties. The method utilizes a novel image-processing algorithm that takes as input a reconstructed volume image that can be acquired by medical imaging scanner such as a micro computed tomography device (microCT), a magnetic resonance imaging (MRI) device, or a . The 3D image data is then analyzed to reveal important morphological parameters of the sample such as bone volume fraction, bone density, bone mineral content and cortical bone thickness. These properties can be then further compared to a reference model in order to extract potential pathological manifestations of the sample, including Osteoporosis-related features.