Trabecular bone (TB) is a complex quasi-random network of interconnected plates and rods. TB constantly remodels to adapt to the stresses to which it is subjected (Wolff s Law). In osteoporosis, this dynamic equilibrium between bone formation and resorption is perturbed, leading to bone loss and structural deterioration, both increasing fracture risk. Bone s mechanical behavior can only be partially explained by variations in bone mineral density, which led to the notion of bone structural quality. Previously, we developed digital topological analysis (DTA) which classifies plates, rods, profiles, edges, and junctions in a TB skeletal representation. Although the method has become quite popular, a major limitation of DTA is that it provides only hard classifications of different topological entities, failing to distinguish between narrow and wide plates. Here, we present a new method called volumetric topological analysis (VTA) for regional quantification of TB topology. At each TB location, the method uniquely classifies its topology on the continuum between perfect plates and perfect rods, facilitating early detections of TB alterations from plates to rods according to the known etiology of osteoporotic bone loss. Several new ideas, including manifold distance transform, manifold scale, and feature propagation have been introduced here and combined with existing DTA and distance transform methods, leading to the new VTA technology. This method has been applied to multi-detector CT and μCT images of four cadaveric distal tibia and five distal radius specimens. Both intra- and inter-modality reproducibility of the method has been examined using repeat CT and μCT scans of distal tibia specimens. Also, the method s ability to predict experimental biomechanical properties of TB via CT imaging under in vivo conditions has been quantitatively examined and the results found are very encouraging.

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Summary People with spinal cord injury (SCI) lose bone and muscle integrity after their injury. Early doses of stress, applied through electrically induced muscle contractions, preserved bone density at high-risk sites. Appropriately prescribed stress early after the injury may be an important consideration to prevent bone loss after SCI. Introduction Skeletal muscle force can deliver high compressive loads to bones of people with spinal cord injury (SCI). The effective osteogenic dose of load for the distal femur, a chief site of fracture, is unknown. The purpose of this study is to compare three doses of bone compressive loads at the distal femur in individuals with complete SCI who receive a novel stand training intervention. Methods Seven participants performed unilateral quadriceps stimulation in supported stance [150% body weight (BW) compressive load—“High Dose” while opposite leg received 40% BW—“Low Dose”]. Five participants stood passively without applying quadriceps electrical stimulation to either leg (40% BW load—“Low Dose”). Fifteen participants performed no standing (0% BW load—“Untrained”) and 14 individuals without SCI provided normative data. Participants underwent bone mineral density (BMD) assessment between one and six times over a 3-year training protocol. Results BMD for the High Dose group significantly exceeded BMD for both the Low Dose and the Untrained groups (p<0.05). No significant difference existed between the Low Dose and Untrained groups (p>0.05), indicating that BMD for participants performing passive stance did not differ from individuals who performed no standing. High-resolution CT imaging of one High Dose participant revealed 86% higher BMD and 67% higher trabecular width in the High Dose limb. Conclusion Over 3 years of training, 150% BW compressive load in upright stance significantly attenuated BMD decline when compared to passive standing or to no standing. High-resolution CT indicated that trabecular architecture was preserved by the 150% BW dose of load.

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Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations.