Improvement In Spatial Accuracy Research Articles

Benefits of a Light-Painting Technique for Learning to Write New Characters: A Proof of Concept With Adults.

Learning to write relies on the effective integration of sensory feedback and a transition from motor control based on written tracings to motor control based on expert writing movements. This study aimed to test whether the photographic technique of light painting (LP) would facilitate this transition. To achieve this, we conducted two experiments using different LP setups. LP involves moving a light source in a dark environment while taking a long-exposure photograph. LP entails both a real-time reduction of product-related visual information and a post-trial addition of process-related visual information. In the first experiment, we conducted a pre-test, training, and post-test in which 16 adults wrote four new characters with the non-dominant hand. During the training sessions, participants stood and wrote in a vertical frame (1 × 1.2m) two characters in the control condition (with a marker on the vertical support) and two characters in the LP condition (with a flashlight in the air). In the test phases, participants were seated at a table and copied the four characters into a square (4cm * 4cm) on a fixed sheet of graphics paper. As in-air writing strongly differs from classical handwriting situations, we performed a second LP experiment. The aim was to implement LP training in a condition closer to writing. Sixteen new participants followed the same protocol but sat at a table and wrote in a horizontal square (20cm * 20cm). In both experiments, participants who trained with the LP method wrote faster and with less pressure than those trained in the control condition. We also observed an improvement in spatial accuracy in Experiment 2, whatever the training condition. LP seemed to have led participants to focus on the writing process, probably because it modified the nature and timing of the visual information used for writing. LP may be a promising technique for remediating writing difficulties.

Regional Digest Aggregation based on Opportunities in Wireless Sensor Networks

It is desirable to reduce the amount of data collected in sensor networks to reduce energy consumption and to extend network lifetime while one should extract as much information as possible to allow support for heterogeneous user queries. There have been a number of aggregation proposals, aiming at reducing the amount of data communications within the sensor network, mainly focused on supporting limited and simple types of queries such as SUM, COUNT, AVG, MIN/MAX. Unfortunately, user queries are not limited by these simple types of aggregates and cannot be predicted a-priori. In this paper we propose an aggregation-framework that produces regional digests at a parameter defined granularity such that arbitrary user queries can be supported. Since the success of the aggregation policy greatly depends on the integrated routing mechanisms we evaluate the performance of our approach under alternative routing approaches. Our experimental results suggest at least 3-fold improvement in spatial accuracy at a relatively small expense of increased energy consumption.

Acoustic wave propagation with new spatial implicit and temporal high-order staggered-grid finite-difference schemes

AbstractThe implicit staggered-grid (SG) finite-difference (FD) method can obtain significant improvement in spatial accuracy for performing numerical simulations of wave equations. Normally, the second-order central grid FD formulas are used to approximate the temporal derivatives, and a relatively fine time step has to be used to reduce the temporal dispersion. To obtain high accuracy both in space and time, we propose a new spatial implicit and temporal high-order SG FD stencil in the time–space domain by incorporating some additional grid points to the conventional implicit FD one. Instead of attaining the implicit FD coefficients by approximating spatial derivatives only, we calculate the coefficients by approximating the temporal and spatial derivatives simultaneously through matching the dispersion formula of the seismic wave equation and compute the FD coefficients of our new stencil by two schemes. The first one is adopting a variable substitution-based Taylor-series expansion (TE) to derive the FD coefficients, which can attain (2M + 2)th-order spatial accuracy and (2N)th-order temporal accuracy. Note that the dispersion formula of our new stencil is non-linear with respect to the axial and off-axial FD coefficients, it is complicated to obtain the optimal spatial and temporal FD coefficients simultaneously. To tackle the issue, we further develop a linear optimisation strategy by minimising the L2-norm errors of the dispersion formula to further improve the accuracy. Dispersion analysis, stability analysis and modelling examples demonstrate the accuracy, stability and efficiency advantages of our two new schemes.

Diffusion-Weighted Echo Planar Imaging using MUltiplexed Sensitivity Encoding and Reverse Polarity Gradient in Head and Neck Cancer: An Initial Study.

We aimed to compare the geometric distortion (GD) correction performance and apparent diffusion coefficient (ADC) measurements of single-shot diffusion-weighted echo-planar imaging (SS-DWEPI), multiplexed sensitivity encoding (MUSE)-DWEPI, and MUSE-DWEPI with reverse-polarity gradient (RPG) in phantoms and patients. We performed phantom studies at 3T magnetic resonance imaging (MRI) using the American College of Radiology phantom and Quantitative Imaging Biomarker Alliance DW-MRI ice-water phantom to assess GD and effect of distortion in the measurement of ADC, respectively. Institutional review board approved the prospective clinical component of this study. DW-MRI data were obtained from 11 patients with head and neck cancer using these three DW-MRI methods. Wilcoxon signed-rank (WSR) and Kruskal–Wallis (KW) tests were used to compare ADC values, and qualitative rating by radiologist between three DW-MRI methods. In the ACR phantom, GD of 0.17% was observed for the b = 0 s/mm2 image of the MUSE-DWEPI with RPG method compared with that of 1.53% and 2.1% of MUSE-DWEPI and SS-DWEPI, respectively; The corresponding methods root-mean-square errors were 0.58, 3.37, and 5.07 mm. WSR and KW tests showed no significant difference in the ADC measurement between these three DW-MRI methods for both healthy masseter muscles and neoplasms (P > .05). We observed improvement in spatial accuracy for MUSE-DWEPI with RPG in the head and neck region with a higher correlation (R2 = 0.791) compared with that for SS-DWEPI (R2 = 0.707) and MUSE-DWEPI (R2 = 0.745). MUSE-DWEPI with RPG significantly reduces the distortion compared with MUSE-DWEPI or conventional SS-DWEPI techniques, and the ADC values were similar.

A New fMRI Informed Mixed-Norm Constrained Algorithm for EEG Source Localization

Complementary with electroencephalograph (EEG), functional magnetic resonance imaging (fMRI), with high spatial resolution, is powerful at providing prior source locations based on actual brain physiology. It hereby can help improve the accuracy of EEG source localization. However, most of the current methods directly penalize the sources whose fMRI activation probability is low and estimate the sources activities at every time point. Thus, they do not account for the temporal interrelated and non-stationary features of electromagnetic brain signals, and some are too much dependent on the fMRI prior. Here, we propose a new fMRI informed EEG source localization method and is termed fMRI-informed spatio-temporal unifying tomography (FIST). It uses a mixed norm constraint defined in terms of time–frequency decomposition of the sources and combines it with fMRI prior. The Fast Iterative Shrinkage Thresholding Algorithm is employed to solve the optimization problem. Both simulated and real EEG data are applied to assess the performance of the proposed method. Compared with L2-norm constrained methods, FIST has the superiority brain source estimation both in the spatial and temporal domains. By virtue of the fMRI information as a prior, FIST has great improvement in spatial accuracy and computational efficiency, when comparing with the method which only uses mixed-norm constraint. In addition, FIST shows good ability to select the fMRI priors to get a better estimation without totally depending on the prior, when comparing with the method which also has fMRI prior information.

WE-G-217A-07: Increased MR Spatial Accuracy with Improved Gradient Nonlinearity and Magnet Inhomogeneity Correction.

To develop improved distortion correction of MR images based on higher degree spherical harmonic models of the gradient system and the main magnetic field. The induced field gradient along all three axes can be modeled by first order spherical harmonics. These models provide a true encoding of the physical location of a spin to the frequency at which it is detected. Currently on many commercial systems, only the lower 5 degrees of the model are used for gradient nonlinearity correction. While this provides sufficient accuracy for diagnostic imaging, the gradient nonlinearity correction was extended to include all first order harmonics up to the 9th degree to improve the spatial accuracy of the images. Using zeroth degree spherical harmonics up to the 20th order, a model of the main magnetic field was also incorporated into the correction algorithm. Shifts caused by field inhomogeneity were calculated using knowledge of the receiver bandwidth, frequency encode direction, and the magnetic field at any given point. These corrections were applied to images of a 50 cm diameter phantom, acquired with an extended FOV 3D FGRE sequence. Any improvements in spatial accuracy were measured in the resulting images. Visual improvements in spatial accuracy were observed with both corrections. With standard gradient nonlinearity correction, edges of the phantom were distorted in a wave-like fashion. With accurate models, almost all of the errors at the edges of the phantom were corrected when both gradient and field homogeneity corrections were applied. With accurate models of the gradient and magnetic field, the two greatest system-induced spatial errors can be corrected. This improved spatial accuracy enables the use of widebore MR scanners for therapy planning on large FOV images and guidance of percutaneous devices. Further applications include extended FOV imaging for combined PET-MR systems. All authors are employed by General Electric Healthcare.

Fast Bayesian scan statistics for multivariate event detection and visualization

The multivariate Bayesian scan statistic (MBSS) is a recently proposed, general framework for event detection and characterization in multivariate space-time data. MBSS integrates prior information and observations from multiple data streams in a Bayesian framework, computing the posterior probability of each type of event in each space-time region. MBSS has been shown to have many advantages over previous event detection approaches, including improved timeliness and accuracy of detection, easy interpretation and visualization of results, and the ability to model and accurately differentiate between multiple event types. This work extends the MBSS framework to enable detection and visualization of irregularly shaped clusters in multivariate data, by defining a hierarchical prior over all subsets of locations. While a naive search over the exponentially many subsets would be computationally infeasible, we demonstrate that the total posterior probability that each location has been affected can be efficiently computed, enabling rapid detection and visualization of irregular clusters. We compare the run time and detection power of this 'Fast Subset Sums' method to our original MBSS approach (assuming a uniform prior over circular regions) on semi-synthetic outbreaks injected into real-world Emergency Department data from Allegheny County, Pennsylvania. We demonstrate substantial improvements in spatial accuracy and timeliness of detection, while maintaining the scalability and fast run time of the original MBSS method.