Towards Image Guided Magnetic Resonance Elastography via Active Driver Positioning Robot.

Magnetic Resonance Elastography (MRE) is a developing imaging technique that enables non-invasive estimation of tissue mechanical properties through the combination of induced mechanical displacements in the tissue and Magnetic Resonance Imaging (MRI). The mechanical drivers necessary to produce shear waves in the tissue have been a focus of engineering effort in the development and refinement of MRE. The potential targeting of smaller and stiffer tissues calls for increases in actuation frequency and refinement of mechanical driver positioning. Furthermore, the anisotropic nature of soft tissues results in driver position related changes in observed displacement wave patterns. These challenges motivate the investigation and development of the concept of active MRE driver positioning through visual servoing under MR imaging. OBJECTIVE: This work demonstrates the initial prototype of an MRE driver positioning system, allowing capture of displacement wave patterns from various mechanical vibration loading angles under different vibration frequencies through MR imaging. METHODS: Three different configurations of the MRE driver positioning robot are tested with an intervertebral disc (IVD) shaped gel phantom. RESULTS: Both the octahedral shear stress signal to noise ratio (OSS-SNR) and estimated stiffness show statistically significant dependence on driver configuration in each of the three phantom IVD regions. CONCLUSION: This dependence demonstrates that driver configuration is a critical factor in MRE, and that the developed robot is capable of producing a range of configurations. SIGNIFICANCE: This work presents the first demonstration of an active, imaging guided MRE driver positioning system, with significance for the future application of MRE to a wider range of human tissues.

An Overview of the Effectiveness of Bicycle Helmet Designs in Impact Testing.

Cycling accidents are the leading cause of sports-related head injuries in the US. Conventional bicycle helmets typically consist of polycarbonate shell over Expanded Polystyrene (EPS) foam and are tested with drop tests to evaluate a helmet's ability to reduce head kinematics. Within the last decade, novel helmet technologies have been proposed to mitigate brain injuries during bicycle accidents, which necessitates the evaluation of their effectiveness in impact testing as compared to conventional helmets. In this paper, we reviewed the literature to collect and analyze the kinematic data of drop test experiments carried out on helmets with different technologies. In order to provide a fair comparison across different types of tests, we clustered the datasets with respect to their normal impact velocities, impact angular momentum, and the type of neck apparatus. When we analyzed the data based on impact velocity and angular momentum clusters, we found that the bicycle helmets that used rotation damping based technology, namely MIPS, had significantly lower peak rotational acceleration (PRA) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) as compared to the conventional EPS liner helmets (p < 0.01). SPIN helmets had a superior performance in PRA compared to conventional helmets (p < 0.05) in the impact angular momentum clustered group, but not in the impact-velocity clustered comparisons. We also analyzed other recently developed helmets that primarily use collapsible structures in their liners, such as WaveCel and Koroyd. In both of the impact velocity and angular momentum groups, helmets based on the WaveCel technology had significantly lower peak linear acceleration (PLA), PRA, and GAMBIT at low impact velocities as compared to the conventional helmets, respectively (p < 0.05). The protective gear with the airbag technology, namely Hövding, also performed significantly better compared to the conventional helmets in the analyzed kinematic-based injury metrics (p < 0.001), possibly due to its advantage in helmet size and stiffness. We also observed that the differences in the kinematic datasets strongly depend on the type of neck apparatus. Our findings highlight the importance and benefits of developing new technologies and impact testing standards for bicycle helmet designs for better prevention of traumatic brain injury (TBI).