Three-Dimensional (3D) Animation and Calculation for the Assessment of Engaging Hill-Sachs Lesions With Computed Tomography 3D Reconstruction.

PURPOSE: To dynamically assess for Hill-Sachs engagement with animated 3-dimensional (3D) shoulder models. METHODS: We created 3D shoulder models from reconstructed computed tomography (CT) images from a consecutive series of patients with recurrent anterior dislocation. They were divided into 2 groups based on the perceived Hill-Sachs severity. For our cohort of 14 patients with recurrent anterior dislocation, 4 patients had undergone osteoarticular allografting of Hill-Sachs lesions and 10 control patients had undergone CT scanning to quantify bone loss but no treatment for bony pathology. A biomechanical analysis was performed to rotate each 3D model using local coordinate systems to the classical vulnerable position of the shoulder (abduction = 90°, external rotation = 0-135°) and through a functional range. A Hill-Sachs lesion was considered "dynamically" engaging if the angle between the lesion's long axis and anterior glenoid was parallel. Results: In the vulnerable position of the shoulder, none of the Hill-Sachs lesions aligned with the anterior glenoid in any of our patients. However, in our simulated physiological shoulder range, all allograft patients and 70% of controls had positions producing alignment. CONCLUSIONS: The technique offers a visual representation of an engaging Hill-Sachs using 3D-animated reconstructions with open-source software and CT images. In our series of patients, we found multiple shoulder positions that align the Hill-Sachs and glenoid axes that do not necessarily meet the traditional definition of engagement. Identifying all shoulder positions at risk of "engaging," in a broader physiological range, may have critical implications toward selecting the appropriate surgical management of bony defects. LEVEL OF EVIDENCE: level III, case-control study.

MARIN: an open-source mobile augmented reality interactive neuronavigation system.

PURPOSE: Neuronavigation systems making use of augmented reality (AR) have been the focus of much research in the last couple of decades. In recent years, there has been considerable interest in using mobile devices for AR in the operating room (OR). We propose a complete system that performs real-time AR video augmentation on a mobile device in the context of image-guided neurosurgery. METHODS: MARIN (mobile augmented reality interactive neuronavigation system) improves upon the state of the art in terms of performance, allowing real-time augmentation, and interactivity by allowing users to interact with the displayed data. The system was tested in a user study with 17 subjects for qualitative and quantitative evaluation in the context of target localization and brought into the OR for preliminary feasibility tests, where qualitative feedback from surgeons was obtained. RESULTS: The results of the user study showed that MARIN performs significantly better in terms of both time ([Formula: see text]) and accuracy ([Formula: see text]) for the task of target localization in comparison with a traditional image-guided neurosurgery (IGNS) navigation system. Further, MARIN AR visualization was found to be more intuitive and allowed users to estimate target depth more easily. CONCLUSION: MARIN improves upon previously proposed mobile AR neuronavigation systems with its real-time performance, higher accuracy, full integration in the normal workflow and greater interactivity and customizability of the displayed information. The improvement in efficiency and usability over previous systems will facilitate bringing AR into the OR.

Manual Cultivation Operations in Poplar Stands: A Characterization of Job Difficulty and Risks of Health Impairment.

Short rotation poplar forests are a viable alternative in producing high quality wood for industrial applications. Their success depends on timely and high-quality implementation of a series of operations. Weed control operations are implemented to favor the trees in their competition for soil resources, and cultivation is an option typically used in many European countries. For the moment, a complete mechanization of such operations is virtually impossible, and they still require an intensive use of manual labor. Since information on work difficulty and risks in manual cultivation operations is limited, this study aimed to characterize this job. Evaluation was made in terms of work efficiency, cardiovascular workload, work intensity and postural risks by implementing a time and motion study combined with heart rate measurements, accelerometry and whole-body postural analysis. Work efficiency was particularly low even if the share of effective work time was high (70% of the observation time). Job was characterized as moderate to high intensity, which resulted into a moderate to high cardiovascular strain. While the postural analysis indicated rather small risks, the main problem was found for the back postures assumed during the work. Improvements should aim to extend mechanization, train the workers and appropriately design rest breaks.

The Discriminative Power of Shape an Empirical Study in Time Series Matching.

Shape provides significant discriminating power in time series matching of visual or geometric data as required in many important applications in graphics and vision. The well established dynamic time warping (DTW) algorithm and its variants do this matching by determining a non-linear time mapping to minimise euclidean distances between corresponding time-warped points. However the shape of curves is not considered. In this paper, we present a new shape-aware algorithm which uses time and shape correspondence (TSC) at increasing levels of detail to define a similarity measure with an norm to aggregate the results, making it robust to noise and missing data. The norm is implicitly regularised using a shape-based error. Through extensive experiments we empirically show that our algorithm outperforms existing state of the art algorithms, works more effectively with high dimensional data, and handles noise and missing data better. We demonstrate its versatile applicability and comparative performance using a large in-house created gait data base, an action data base from Microsoft, exercise action data from a local company, a large public time series data base from University of California, Riverside and hand movement in quaternion stream data format.

Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system.

In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors' method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.

Quantifying attention shifts in augmented reality image-guided neurosurgery.

Image-guided surgery (IGS) has allowed for more minimally invasive procedures, leading to better patient outcomes, reduced risk of infection, less pain, shorter hospital stays and faster recoveries. One drawback that has emerged with IGS is that the surgeon must shift their attention from the patient to the monitor for guidance. Yet both cognitive and motor tasks are negatively affected with attention shifts. Augmented reality (AR), which merges the realworld surgical scene with preoperative virtual patient images and plans, has been proposed as a solution to this drawback. In this work, we studied the impact of two different types of AR IGS set-ups (mobile AR and desktop AR) and traditional navigation on attention shifts for the specific task of craniotomy planning. We found a significant difference in terms of the time taken to perform the task and attention shifts between traditional navigation, but no significant difference between the different AR set-ups. With mobile AR, however, users felt that the system was easier to use and that their performance was better. These results suggest that regardless of where the AR visualisation is shown to the surgeon, AR may reduce attention shifts, leading to more streamlined and focused procedures.