New Technology for Shallow Water Hydrographic Surveys

The United States Office of Coast Survey is developing technology for shallow water hydrographic surveys in order to increase the efficiency with which hydrographic data are acquired and to improve the likelihood that all potential dangers to navigation are detected in the course of a hydrographic survey. Three areas of technology hold the greatest promise for meeting those goals: Airborne Lidar Hydrography (ALH), Shallow Water Multibeam Sonars (SWMB), and digital side scan sonar, especially the Coast Survey’s new High Speed, High Resolution Side Scan Sonar (HSHRSSS). The Coast Survey expects that all its ALH surveys will be outsourced to private sector contractors, and that its SWMB and side scan sonar surveys will be accomplished by both NOAA survey vessels and by private sector contractors. This diversity of sources for survey data influences the strategy for managing these new technologies

Characterizing and modeling the apparent anomalous behavior of resistivity in Cr–Si–O thin films

The Cr-Si-O material system is of interest for use as a thin film resistor. The films are sputter deposited onto conducting substrates from metal oxide compacts using a reactive gas mixture. the cermet films composition range from 50 to 100 vol.% SiO{sub 2} as determined from elemental measurements of the Cr, Si and O content. In a wide range of resistivities from 10{sup 1} to 10 {sup 14} {omega}-cm measured through the film thickness, an apparent anomalous behavior is found with the Cr, Si and O composition. The anomaly can be deducted to a discontinuous variation of resistivity with film composition near 80 vol.% SiO{sub 2}. The film microstructure is characterized as a distribution of conducting metal-silicide particles within an insulating matrix. The effective medium theory is used to predict the variation of conductivity and successfully models the anomalous resistivity behavior

Nanomechanical control of an optical antenna

Resonant optical nanoantennas hold great promise for applications in physics and chemistry1–6. Their operation relies on their ability to concentrate light on spatial scales much smaller than the wavelength. In this work, we mechanically tune the length and gap between two triangles comprising a single gold bow-tie antenna by precise nanomanipulation with the tip of an atomic force microscope. At the same time, the optical response of the nanostructure is determined by means of dark-field scattering spectroscopy. We find no unique single ‘antenna resonance’. Instead, the plasmon mode splits into two dipole resonances for gap sizes on the order of a few tens of nanometres, governed by the full three-dimensional shape of the antenna arms. This result opens the door to new nano-optomechanical devices, where mechanical changes on the nanometre scale control the optical properties of artificial structures

pH mapping of brain tissue by a deep neural network trained on 9.4T CEST MRI data: pH-deepCEST

The pH value is of major importance for most physiological processes and may change due to altered metabolism in pathologies. In the present work, we exploit the inherent dependency of CEST MR data on pH with a new approach: train neural networks to map voxel-by-voxel from multi-B1+ CEST spectra to pH value. Measurements were performed in homogenate of pig brain tissue at 9.4T ultra high field. Prediction of absolute pH values was possible and predictions were stable against inhomogeneity in B1+. We hope this proof of concept might be a first small step towards high-resolution 3D pH maps in vivo

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

Machine learning based processing of magnetic resonance data, including an uncertainty quantification

A method of processing magnetic resonance data of a sample under investigation includes the steps ofprovision of the MR data being collected with an MRI scanner apparatus, and machine learning based data analysis of the MR data by supplying the MR data to an artificial neural network being trained with predetermined training data, wherein at least one image parameter of the sample and additionally at least one uncertainty quantification measure representing a pre diction error ofthe at least one image parameter are provided by output elements of the neural network. Furthermore, a magnetic resonance imaging (MRI) scanner apparatus being adapted for employing the method ofprocessing MR data is described

comprehenCEST: a clinically feasible CEST protocol to cover all existing CEST preparation schemes by snapshot readout and reduction of overhangs

INTRODUCTION: CEST sequences preparing a selected metabolite normally sample the full Z-spectrum, allowing for asymmetry or model- based evaluations. To achieve good labelling efficiency for various metabolites, different physical preparations are required. Sparsity-enforcing machine learning algorithms help to select and combine the differently CEST-prepared images from a sequence pool covering the preparation parameter space, while preserving main contrast information. Together with a fast, single-shot 3D readout, we create a 3D CEST protocol containing 13 established contrasts in 10 minutes scan time. METHODS: (1) 13 contrasts are evaluated conventionally (PCA-denoising, dB0-correction, MTR asymmetry or Lorentz fit) from six existing CEST sequences [2-5], which cover B1cwpe levels from 0.5 to 4 uT, offering the ground truth. Mz is prepared using standardized pulseq-CEST building blocks [7-8], and probed with the fast snapshotCEST 3D readout [1]. (2) All Z-spectra are mapped to contrast via a linear projection, while sparsity-enforcing L1-regularization reduces the number of consumed offsets (rowLASSO [6,10]). The training is carried out on uncorrected, raw Z-spectra to generate a selection that provides robustness against noise and B0/B1 inhomogeneities. (3) Difference maps between ground truth and model output are created for the validation dataset. RESULTS: 5 of the 13 generated CEST maps are shown in Figure 1. Lowering CEST offsets down to 82 still yields similar imaging contrast. The normalized, mean absolute error (NMAE) between linear model and ground truth, averaged over all 13 contrasts, and for retaining offset rate r is: 27 ± 6% (r = 1), 29 ± 7% (r = 0.8), 32 ± 8% (r = 0.6), 36 ± 9% (r = 0.4), 42 ± 11% (r = 0.2). Residual errors visible here might still originate from an observed B0 drift during the whole data acquisition; it is excepted that this can be further improved. DISCUSSION & CONCLUSION: Instead of arguing which is the best CEST protocol to provide new insights into a pathology, and only measure one CEST contrast, we suggest measuring them all. By combining sparse sampling and snapshot readout, a comprehensive protocol covering most of the reported labellings of 10 minutes is conceivable. This allows to design powerful hypotheses generating clinical pilot studies

Can a neural network predict B0 maps from uncorrected CEST-spectra?

Analysis of chemical exchange saturation transfer (CEST) effects suffers from B0 inhomogeneity. Common correction methods involve computationally expensive algorithms or even additional measurements. Here we demonstrate that deep neural networks are able to predict B0 maps from raw Z-spectra by training the networks with measured B0 maps. Moreover, we show that CEST contrast parameters representing amide, amine and NOE resonance peaks can be directly predicted from uncorrected Z-spectra in a fast single step. This provides a shortcut to conventional evaluation procedures and will be useful to guide nonlinear model fitting

MR-double-zero: Can a machine discover new MRI contrasts, such as metabolite concentration?

Discovery of MR contrast and/or conventional sequence parameter optimization usually requires a theoretical model to describe MR physics. Here we investigate if novel contrasts can be found by directly running numerical optimization on a real MRI scanner instead of a simulation. To this end, a derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the scanner and map the acquired signals to a given target contrast. As proof-of-principle, we show that this enables creatine concentration mapping by learning a CEST-prepared sequence, which is found solely based on known target concentrations in a phantom