Computationally-Optimized Bone Mechanical Modeling from High-Resolution Structural Images

Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging

Non-invasive Predictors of Human Cortical Bone Mechanical Properties: T2-Discriminated 1H NMR Compared with High Resolution X-ray

Recent advancements in magnetic resonance imaging (MRI) have enabled clinical imaging of human cortical bone, providing a potentially powerful new means for assessing bone health with molecular-scale sensitivities unavailable to conventional X-ray-based diagnostics. To this end, 1H nuclear magnetic resonance (NMR) and high-resolution X-ray signals from human cortical bone samples were correlated with mechanical properties of bone. Results showed that 1H NMR signals were better predictors of yield stress, peak stress, and pre-yield toughness than were the X-ray derived signals. These 1H NMR signals can, in principle, be extracted from clinical MRI, thus offering the potential for improved clinical assessment of fracture risk

The Founder’s Lecture 2009: advances in imaging of osteoporosis and osteoarthritis

The objective of this review article is to provide an update on new developments in imaging of osteoporosis and osteoarthritis over the past three decades. A literature review is presented that summarizes the highlights in the development of bone mineral density measurements, bone structure imaging, and vertebral fracture assessment in osteoporosis as well as MR-based semiquantitative assessment of osteoarthritis and quantitative cartilage matrix imaging. This review focuses on techniques that have impacted patient management and therapeutic decision making or that potentially will affect patient care in the near future. Results of pertinent studies are presented and used for illustration. In summary, novel developments have significantly impacted imaging of osteoporosis and osteoarthritis over the past three decades

Search for Dijet Resonances in 7 TeV pp Collisions at CMS (vol 105, 211801, 2010)

Publisher’s Note: Search for Dijet Resonances in 7 TeV pp Collisions at CMS [Phys. Rev. Lett. 105, 211801 (2010)