An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT

Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial resolution of QCT. We propose a direct model based method to automatically identify the surface through the center of the cortex of human vertebra. We develop a statistical bone model and analyze its probability distribution after the imaging process. Using an as-rigid-as-possible deformation we find the cortical surface that maximizes the likelihood of our model given the input volume. Using the European Spine Phantom (ESP) and a high resolution \mu CT scan of a cadaveric vertebra, we show that the proposed method is able to accurately identify the real center of cortex ex-vivo. To demonstrate the in-vivo applicability of our method we use manually obtained surfaces for comparison.Comment: Presented on German Conference on Pattern Recognition (GCPR) 2018 in Stuttgar

Exploring the effects of replicating shape, weight and recoil effects on VR shooting controllers

Commercial Virtual Reality (VR) controllers with realistic force feedback are becoming available, to increase the realism and immersion of first-person shooting (FPS) games in VR. These controllers attempt to mimic not only the shape and weight of real guns but also their recoil effects (linear force feedback parallel to the barrel, when the gun is shot). As these controllers become more popular and affordable, this paper investigates the actual effects that these properties (shape, weight, and especially directional force feedback) have on performance for general VR users (e.g. users with no marksmanship experience), drawing conclusions for both consumers and device manufacturers. We created a prototype replicating the properties exploited by commercial VR controllers (i.e. shape, weight and adjustable force feedback) and used it to assess the effect of these parameters in user performance, across a series of user studies. We first analysed the benefits on user performance of adding weight and shape vs a conventional controller (e.g. Vive controller). We then explore the implications of adding linear force feedback (LFF), as well as replicating the shape and weight. Our studies show negligible effects on the immediate shooting performance with some improvements in subjective appreciation, which are already present with low levels of LFF. While higher levels of LFF do not increase subjective appreciations any further, they lead users to reach their maximum distance skillset more quickly. This indicates that while adding low levels of LFF can be enough to influence user’s immersion/engagement for gaming contexts, controllers with higher levels of LFF might be better suited for training environments and/or when dealing with particularly demanding aiming tasks