Estimation of the lateral correlation structure of subsurface water content from surface-based ground-penetrating radar reflection images

Over the past decade, significant interest has been expressed in relating the spatial statistics of surface-based reflection ground-penetrating radar (GPR) data to those of the imaged subsurface volume. A primary motivation for this work is that changes in the radar wave velocity, which largely control the character of the observed data, are expected to be related to corresponding changes in subsurface water content. Although previous work has indeed indicated that the spatial statistics of GPR images are linked to those of the water content distribution of the probed region, a viable method for quantitatively analyzing the GPR data and solving the corresponding inverse problem has not yet been presented. Here we address this issue by first deriving a relationship between the 2-D autocorrelation of a water content distribution and that of the corresponding GPR reflection image. We then show how a Bayesian inversion strategy based on Markov chain Monte Carlo sampling can be used to estimate the posterior distribution of subsurface correlation model parameters that are consistent with the GPR data. Our results indicate that if the underlying assumptions are valid and we possess adequate prior knowledge regarding the water content distribution, in particular its vertical variability, this methodology allows not only for the reliable recovery of lateral correlation model parameters but also for estimates of parameter uncertainties. In the case where prior knowledge regarding the vertical variability of water content is not available, the results show that the methodology still reliably recovers the aspect ratio of the heterogeneity.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Federalism and Welfare to Work in Switzerland: The Development of Active Social Policies in a Fragmented Welfare State

This article is concerned with the impact that federal structures have on the development of welfare to work or activation policies. More precisely, it argues that the incentives and the risks associated with a division of responsibilities among different jurisdictions may constitute an obstacle to broad reforms that promote labor market participation of nonworking benefit recipients. This argument is illustrated with a case study discussing policy responses to a massive rise in caseloads among social assistance recipients in Switzerland. We conclude that the lack of a fundamental reform was the consequence of the incentives provided by the federal structure of the program. These incentives have both encouraged cost shifting among jurisdictions and discouraged involvement of federal level policy makers in a bigger reform.

Free-breathing renal magnetic resonance angiography with steady-state free-precession and slab-selective spin inversion combined with radial k-space sampling and water-selective excitation.

The impact of radial k-space sampling and water-selective excitation on a novel navigator-gated cardiac-triggered slab-selective inversion prepared 3D steady-state free-precession (SSFP) renal MR angiography (MRA) sequence was investigated. Renal MRA was performed on a 1.5-T MR system using three inversion prepared SSFP approaches: Cartesian (TR/TE: 5.7/2.8 ms, FA: 85 degrees), radial (TR/TE: 5.5/2.7 ms, FA: 85 degrees) SSFP, and radial SSFP combined with water-selective excitation (TR/TE: 9.9/4.9 ms, FA: 85 degrees). Radial data acquisition lead to significantly reduced motion artifacts (P < 0.05). SNR and CNR were best using Cartesian SSFP (P < 0.05). Vessel sharpness and vessel length were comparable in all sequences. The addition of a water-selective excitation could not improve image quality. In conclusion, radial k-space sampling reduces motion artifacts significantly in slab-selective inversion prepared renal MRA, while SNR and CNR are decreased. The addition of water-selective excitation could not improve the lower CNR in radial scanning.

Coronary MR imaging using free-breathing 3D steady-state free precession with radial k-space sampling.

PURPOSE: To investigate the potential of free-breathing 3D steady-state free precession (SSFP) imaging with radial k-space sampling for coronary MR-angiography (MRA), coronary projection MR-angiography and coronary vessel wall imaging. MATERIALS AND METHODS: A navigator-gated free-breathing T2-prepared 3D SSFP sequence (TR = 6.1 ms, TE = 3.0 ms, flip angle = 120 degrees, field-of-view = 360 mm(2)) with radial k-space sampling (384 radials) was implemented for coronary MRA. For projection coronary MRA, this sequence was combined with a 2D selective aortic spin tagging pulse. Coronary vessel wall imaging was performed using a high-resolution inversion-recovery black-blood 3D radial SSFP sequence (384 radials, TR = 5.3 ms, TE = 2.7 ms, flip angle = 55 degrees, reconstructed resolution 0.35 x 0.35 x 1.2 mm(3)) and a local re-inversion pulse. Six healthy volunteers (two for each sequence) were investigated. Motion artifact level was assessed by two radiologists. Results: In coronary MRA, the coronary lumen was displayed with a high signal and high contrast to the surrounding lumen. Projection coronary MRA demonstrated selective visualization of the coronary lumen while surrounding tissue was almost completely suppressed. In coronary vessel wall imaging, the vessel wall was displayed with a high signal when compared to the blood pool and the surrounding tissue. No visible motion artifacts were seen. Conclusion: 3D radial SSFP imaging enables coronary MRA, coronary projection MRA and coronary vessel wall imaging with a low motion artifact level.

Predictors of return to work after a knee injury in patients hospitalized in vocational rehabilitation

Introduction.- Knee injuries are frequent in a young and active population. Most of the patients resume their professional activity but few studies were interested in factors that predict a return to work. The aim of this study is to identify these predictors from a large panel of bio-psychosocial variables. We postulated that the return to work 3 months and 2 years after discharge is mostly predicted by psychosocial variables.Patients and methods.- Prospective study, patients hospitalized for a knee injury. Variables measured: the abbreviated injury score (AIS) for the gravity of the injuries, analog visual scale for the intensity of pain, INTERMED for the bio-psychosocial complexity, SF-36 for the quality of life, HADs for the anxiety/depression symptoms and IKDC score for the knee function. Univariate logistic regressions, adjusted for age and gender, were performed in order to predict return to work.Results.- One hundred and twenty-six patients hospitalized during 8 months after the accident were included into this prospective study. A total of 73 (58%) and 75 (59%) questionnaires were available after 3 months and 2 years, respectively. The SF-36 pain was the sole predictor of return to work at 3 months (odds Ratio 1.06 [1.02-1.10], P = 0.01; for a one point increase) and 2 years (odds Ratio 1.06 [1.02-1.10], P = 0.01). At three months, other factors are SF-36 (physic sub-scale), IKDC score, the presence of a work contract and the presence of litigation. The bio-psychosocial complexity, the presence of depressive symptoms predicts the return to work at two years.Discussion.- Our working hypothesis was partially confirmed: some psychosocial factors (i.e. depressive symptoms, work contract, litigation, INTERMED) predict the return to work but the physical health and the knee function, perceived by the patient, are also correlated. Pain is the sole factor isolated at both times (i.e. 3 months and 2 years) and, consequently, appears a key element in the prediction of the return to work. Some factors are accessible to the rehabilitation program but only if an interdisciplinary approach is performed.

The effect of recalled previous work environment on return to work after a rehabilitation program including vocational aspects for trauma patients.

INTRODUCTION: The aim of the present study was to assess the association between remembered previous work place environment and return to work (RTW) after hospitalisation in a rehabilitation hospital. METHODS: A cohort of 291 orthopedic trauma patients discharged from hospital between 15 December 2004 and 31 December 2005 was included in a study addressing quality of life and work-related questions. Remembered previous work environment was measured by Karasek's 31-item Job Content Questionnaire (JCQ), given to the patients during hospitalisation. Post-hospitalisation work status was assessed 3 months, 1, and 2 years after discharge, using a questionnaire sent to the ex-patients. Logistic regression models were used to test the role of four JCQ variables on RTW at each time point while controlling for relevant confounders. RESULTS: Subjects perceiving a higher physical demand were less likely to return to work 1 year after hospital discharge. Social support at work was positively associated with RTW at all time points. A high job strain appeared to be positively associated with RTW 1 year after rehabilitation, with limitations due to large confidence intervals. CONCLUSIONS: Perceptions of previous work environment may influence the probability of RTW. In a rehabilitation setting, efforts should be made to assess those perceptions and, if needed, interventions to modify them should be applied.

Image quality assessment in digital mammography: part I. Technical characterization of the systems.

In many European countries, image quality for digital x-ray systems used in screening mammography is currently specified using a threshold-detail detectability method. This is a two-part study that proposes an alternative method based on calculated detectability for a model observer: the first part of the work presents a characterization of the systems. Eleven digital mammography systems were included in the study; four computed radiography (CR) systems, and a group of seven digital radiography (DR) detectors, composed of three amorphous selenium-based detectors, three caesium iodide scintillator systems and a silicon wafer-based photon counting system. The technical parameters assessed included the system response curve, detector uniformity error, pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Approximate quantum noise limited exposure range was examined using a separation of noise sources based upon standard deviation. Noise separation showed that electronic noise was the dominant noise at low detector air kerma for three systems; the remaining systems showed quantum noise limited behaviour between 12.5 and 380 µGy. Greater variation in detector MTF was found for the DR group compared to the CR systems; MTF at 5 mm(-1) varied from 0.08 to 0.23 for the CR detectors against a range of 0.16-0.64 for the DR units. The needle CR detector had a higher MTF, lower NNPS and higher DQE at 5 mm(-1) than the powder CR phosphors. DQE at 5 mm(-1) ranged from 0.02 to 0.20 for the CR systems, while DQE at 5 mm(-1) for the DR group ranged from 0.04 to 0.41, indicating higher DQE for the DR detectors and needle CR system than for the powder CR phosphor systems. The technical evaluation section of the study showed that the digital mammography systems were well set up and exhibiting typical performance for the detector technology employed in the respective systems.