Electronic Chart of the Future: The Hampton Roads Project

ECDIS is evolving from a two-dimensional static display of chart-related data to a decision support system capable of providing real-time or forecast information. While there may not be consensus on how this will occur, it is clear that to do this, ENC data and the shipboard display environment must incorporate both depth and time in an intuitively understandable way. Currently, we have the ability to conduct high-density hydrographic surveys capable of producing ENCs with decimeter contour intervals or depth areas. Yet, our existing systems and specifications do not provide for a full utilization of this capability. Ideally, a mariner should be able to benefit from detailed hydrographic data, coupled with both forecast and real-time water levels, and presented in a variety of perspectives. With this information mariners will be able to plan and carry out transits with the benefit of precisely determined and easily perceived underkeel, overhead, and lateral clearances. This paper describes a Hampton Roads Demonstration Project to investigate the challenges and opportunities of developing the “Electronic Chart of the Future.” In particular, a three-phase demonstration project is being planned: 1. Compile test datasets from existing and new hydrographic surveys using advanced data processing and compilation procedures developed at the University of New Hampshire’s Center for Coastal and Ocean Mapping/Joint Hydrographic Center (CCOM/JHC); 2. Investigate innovative approaches being developed at the CCOM/JHC to produce an interactive time- and tide-aware navigation display, and to evaluate such a display on commercial and/or government vessels; 3. Integrate real-time/forecast water depth information and port information services transmitted via an AIS communications broadcast

The consequences of traumatic brain injury from the classroom to the courtroom: understanding pathways through structural equation modelling

Purpose: Paediatric traumatic brain injury (TBI) can have resultant ongoing significant impairments which can impact life outcomes. The primary aim of this research was to explore whether TBI contributes to the relationship between poor educational outcomes and offending trajectories. Materials and methods: Through analysis of a dataset consisting of self-reported health, educational, and offending histories of 70 incarcerated young males, structural equation modelling was used to explore the mediation of educational outcomes and patterns in offending behaviour by chronic symptoms following TBI. Results: Symptoms related to TBI significantly mediated the relationship between decreased educational attainment and more frequent convictions. It did not mediate any relationships involving age at first conviction. Conclusions: Traumatic brain injury appears to have more influence over frequency of offending patterns than age at first conviction. However, TBI remains a pervasive factor in both higher rates of offending and poorer educational attainment. In order to tackle this effect on adverse social outcomes, greater attention to the impact of TBI is required in education and criminal justice systems. IMPLICATIONS FOR REHABILITATION Highlights traumatic brain injury as a contributory factor in some education to offending pathways, suggesting that greater focus on rehabilitation within the education and criminal justice systems is required. Reinforces that greater understanding of educational pathways post-injury is needed to better facilitate rehabilitation within the school system

Quantification of multiple diffusion metrics from asymmetric balanced SSFP frequency profiles using neural networks

Asymmetries in the balanced SSFP frequency profile are known to reflect information about intravoxel tissue microenvironment with strong sensitivity to white matter fiber tract orientation. Phase-cycled bSSFP has demonstrated potential for multi-parametric quantification of relaxation times, static and transmit field inhomogeneity, or conductivity, but has not yet been investigated for diffusion quantification. Therefore, a neural network approach is suggested, which learns a model for voxelwise quantification of diffusion metrics from bSSFP profiles. Not only the feasibility for robust predictions of mean diffusivity (MD) and fractional anisotropy (FA) is shown, but also potential to estimate the principal diffusion eigenvector

Advances in accelerated imaging at 9.4T with electronically modulated time-varying receive sensitivities

Previously, we have shown in simulations that electronically modulated time-varying receive sensitivities can improve parallel imaging reconstruction when fast modulations are applied during acquisition of k-space lines. Here, we demonstrate this concept experimentally with a prototype 8-channel reconfigurable receive coil, for which B1- modulation is achieved by fast switching PIN diodes in the receive loops. With this setup, MR measurements were performed in both phantom and human subject. Lower reconstruction errors and g-factors (~25% improvement for R=4) were observed for the case of rapidly switched sensitivities compared to conventional reconstruction with static sensitivities

Calibration kernels with Alternative Sampling Scheme (CASS) for Parallel Imaging: SENSE meets CASS

We developed new calibration kernels with an alternative undersampling scheme (CASS) for parallel imaging to reduce coherent aliasing artifacts and noises. By sampling k-space lines with irregular and blockwise patterns, incoherent aliasing patterns and noise signals were spread in reconstructed CASS images. Noteworthily, the CASS method outperformed the conventional GRAPPA method at higher acceleration factors