MRI Registration for Human Hip Kinematics

Measurement of joint kinematics can provide knowledge to help improve joint prosthesis design, as well as identify joint motion patterns that may lead to joint degeneration or injury. More investigation into how the hip translates in live human subjects during high amplitude motions is needed. This work presents a design of a non-invasive method using the registration between images from conventional Magnetic Resonance Imaging (MRI) and open MRI to calculate three dimensional hip joint kinematics. The method was tested on a single healthy subject in three different poses. MRI protocols for the conventional gantry, high-resolution MRI and the open gantry, lowresolution MRI were developed. The scan time for the low-resolution protocol was just under 6 minutes. High-resolution meshes and low resolution contours were derived from segmentation of the high-resolution and low-resolution images, respectively. Low-resolution contours described the poses as scanned, whereas the meshes described the bones’ geometries. The meshes and contours were registered to each other, and joint kinematics were calculated. The segmentation and registration were performed for both cortical and sub-cortical bone surfaces. A repeatability study was performed by comparing the kinematic results derived from three users’ segmentations of the sub-cortical bone surfaces from a low-resolution scan. The root mean squared error of all registrations was below 1.92mm. The maximum range between segmenters in translation magnitude was 0.95mm, and the maximum deviation from the average of all orientations was 1.27◦. This work demonstrated that this method for non-invasive measurement of hip kinematics is promising for measuring high-range-of-motion hip motions in vivo.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.