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Spatial Smoothing (spatial + smoothing)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Variable smoothing in Bayesian intrinsic autoregressions

ENVIRONMETRICS, Issue 8 2007
Mark J. Brewer
Abstract We introduce an adapted form of the Markov random field (MRF) for Bayesian spatial smoothing with small-area data. This new scheme allows the amount of smoothing to vary in different parts of a map by employing area-specific smoothing parameters, related to the variance of the MRF. We take an empirical Bayes approach, using variance information from a standard MRF analysis to provide prior information for the smoothing parameters of the adapted MRF. The scheme is shown to produce proper posterior distributions for a broad class of models. We test our method on both simulated and real data sets, and for the simulated data sets, the new scheme is found to improve modelling of both slowly-varying levels of smoothness and discontinuities in the response surface. Copyright © 2007 John Wiley & Sons, Ltd. [source]

The role of the staff MFF in distributing NHS funding: taking account of differences in local labour market conditions

HEALTH ECONOMICS, Issue 5 2010
Robert Elliott
Abstract The National Health Service (NHS) in England distributes substantial funds to health-care providers in different geographical areas to pay for the health care required by the populations they serve. The formulae that determine this distribution reflect populations' health needs and local differences in the prices of inputs. Labour is the most important input and area differences in the price of labour are measured by the Staff Market Forces Factor (MFF). This Staff MFF has been the subject of much debate. Though the Staff MFF has operated for almost 30 years this is the first academic paper to evaluate and test the theory and method that underpin the MFF. The theory underpinning the Staff MFF is the General Labour Market method. The analysis reported here reveals empirical support for this theory in the case of nursing staff employed by NHS hospitals, but fails to identify similar support for its application to medical staff. The paper demonstrates the extent of spatial variation in private sector and NHS wages, considers the choice of comparators and spatial geography, incorporates vacancy modelling and illustrates the effect of spatial smoothing. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Hypothesis testing in distributed source models for EEG and MEG data

HUMAN BRAIN MAPPING, Issue 2 2006
Lourens J. Waldorp
Abstract Hypothesis testing in distributed source models for the electro- or magnetoencephalogram is generally performed for each voxel separately. Derived from the analysis of functional magnetic resonance imaging data, such a statistical parametric map (SPM) ignores the spatial smoothing in hypothesis testing with distributed source models. For example, when intending to test a single voxel, actually an entire region of voxels is tested simultaneously. Because there are more parameters than observations, typically constraints are employed to arrive at a solution which spatially smooths the solution. If ignored, it can be concluded from the hypothesis test that there is activity at some location where there is none. In addition, an SPM on distributed source models gives the illusion of very high resolution. As an alternative, a multivariate approach is suggested in which a region of interest is tested that is spatially smooth. In simulations with MEG and EEG it is shown that clear hypothesis testing in distributed source models is possible, provided that there is high correspondence between what is intended to be tested and what is actually tested. The approach is also illustrated by an application to data from an experiment measuring visual evoked fields when presenting checkerboard patterns. Hum Brain Mapp, 2005. © 2005 Wiley-Liss, Inc. [source]

Reduction of errors in ASL cerebral perfusion and arterial transit time maps using image de-noising

MAGNETIC RESONANCE IN MEDICINE, Issue 3 2010
Jack A. Wells
Abstract In this work, the performance of image de-noising techniques for reducing errors in arterial spin labeling cerebral blood flow and arterial transit time estimates is investigated. Simulations were used to show that the established arterial spin labeling cerebral blood flow quantification method exhibits the bias behavior common to nonlinear model estimates, and as a result, the reduction of random errors using image de-noising can improve accuracy. To assess the effect on precision, multiple arterial spin labeling data sets acquired from the rat brain were processed using a variety of common de-noising methods (Wiener filter, anisotropic diffusion filter, gaussian filter, wavelet decomposition, and independent component analyses). The various de-noising schemes were also applied to human arterial spin labeling data to assess the possible extent of structure degradation due to excessive spatial smoothing. The animal experiments and simulated data show that noise reduction methods can suppress both random and systematic errors, improving both the precision and accuracy of cerebral blood flow measurements and the precision of transit time maps. A number of these methods (and particularly independent component analysis) were shown to achieve this aim without compromising image contrast. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [source]