Spatial auto-regressive analysis of correlation in 3-D PET with application to model-based simulation of data

When a scanner is installed and begins to be used operationally, its actual performance may deviate somewhat from the predictions made at the design stage. Thus it is recommended that routine quality assurance (QA) measurements be used to provide an operational understanding of scanning properties. While QA data are primarily used to evaluate sensitivity and bias patterns, there is a possibility to also make use of such data sets for a more refined understanding of the 3-D scanning properties. Building on some recent work on analysis of the distributional characteristics of iteratively reconstructed PET data, we construct an auto-regression model for analysis of the 3-D spatial auto-covariance structure of iteratively reconstructed data, after normalization. Appropriate likelihood-based statistical techniques for estimation of the auto-regression model coefficients are described. The fitted model leads to a simple process for approximate simulation of scanner performance-one that is readily implemented in an R script. The analysis provides a practical mechanism for evaluating the operational error characteristics of iteratively reconstructed PET images. Simulation studies are used for validation. The approach is illustrated on QA data from an operational clinical scanner and numerical phantom data. We also demonstrate the potential for use of these techniques, as a form of model-based bootstrapping, to provide assessments of measurement uncertainties in variables derived from clinical FDG-PET scans. This is illustrated using data from a clinical scan in a lung cancer patient, after a 3-minute acquisition has been re-binned into three consecutive 1-minute time-frames. An uncertainty measure for the tumor SUVmax value is obtained. The methodology is seen to be practical and could be a useful support for quantitative decision making based on PET data

Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Statistical analysis of positron emission tomography data

Positron emission tomography (PET) is a noninvasive medical imaging tool that produces sequences of images describing the distribution of radiotracers in the object. PET images can be processed to evaluate functional, biochemical, and physiological parameters of interest in human body. However, images generated by PET are generally noisy, thereby complicating their geometric interpretation and affecting the precision. The use of physical models to simulate the performance of PET scanners is well established. Such techniques are particularly useful at the design stage as they allow alternative specifications to be examined. When a scanner is installed and begins to be used operationally, its actual performance may deviate somewhat from the predictions made at the design stage. Thus it is recommended that routine quality assurance (QA) measurements could be used to provide an operational understanding of scanning properties. While QA data are primarily used to evaluate sensitivity and bias patterns, there is a possibility to also make use of such data sets for a more refined understanding of the 3-D scanning properties. Therefore, a comprehensive understanding of the noise characteristics in PET images could lead to improvements in clinical decision making. The main goals of this thesis are to develop model-based approaches for describing and evaluating the statistical properties of noise and a practical approach for simulation of an operational PET scanner. We began with the empirical analysis of statistical characteristics—bias, variance and correlation patterns in a series of operational scanning data. A multiplicative Gamma model had been developed for representing the structure of reconstructed PET data. The novel iteratively re-weighted least squares (IRLS) techniques were proposed for the model fitting. These included the use of a Gamma-based probability transform for normalising residuals, which could be used for model diagnostics. Building on the Gamma based modelling and probability transformation, we developed a 3-D spatial autoregressive (SAR) model to represent the 3-D spatial auto-covariance structure within the normalised data. Auto-regressive coefficients were also estimated based on the minimisation of difference between 3-D auto-correlations calculated from the normalised data and model. Both traditional filtered back-projection (FBP) and expectation-maximisation (EM) reconstructions were considered. Numerical simulation studies were carried out to evaluate the performance of the above models. The proposed models led to a very trivial process for simulation of the scanner—one that can be implemented in R. This provided a very practical mechanism to be routinely used in clinical practice—assessing error characteristics associated with quantified PET measures. Moreover, this fast and simplified approach has a potential usage in enhancing the quality of inferences produced from operational clinical PET scanners

Downscaling regional climate model outputs for the Caribbean using a weather generator

Locally relevant scenarios of daily weather variables that represent the best knowledge of the present climate and projections of future climate change are needed by planners and managers to inform management and adaptation to climate change decisions. Information of this kind for the future is only readily available for a few developed country regions of the world. For many less-developed regions, it is often difficult to find series of observed daily weather data to assist in planning decisions. This study applies a previously developed single-site weather generator (WG) to the Caribbean, using examples from Belize in the west to Barbados in the east. The purpose of this development is to provide users in the region with generated sequences of possible future daily weather that they can use in a number of impact sectors. The WG is first calibrated for a number of sites across the region and the goodness of fit of the WG against the daily station observations assessed. Particular attention is focussed on the ability of the precipitation component of the WG to generate realistic extreme values for the calibration or control period. The WG is then modified using change factors (CFs) derived from regional climate model projections (control and future) to simulate future 30-year scenarios centred on the 2020s, 2050s and 2080s. Changes between the control period and the three futures are illustrated not just by changes in average temperatures and precipitation amounts but also by a number of well-used measures of extremes (very warm days/nights, the heaviest 5-day precipitation total in a month, counts of the number of precipitation events above specific thresholds and the number of consecutive dry days)

A Bayesian Nonparametric model for textural pattern heterogeneity

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumor heterogeneity through patterns of enhancement, texture, morphology, and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of Gray-Level Co-occurrence Matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero-inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine-learning approaches currently used in cancer radiomics with simulation studies.Comment: 45 pages, 7 figures, 1 Tabl

Farmer Organizations, Spatial Effects, and Farm Household Performances: Econometric Evidence from Senegal

In sub-Saharan Africa, agriculture is a significant source of economic growth and the sector has the largest contribution to poverty reduction. But its development is challenged by the need for institutional innovations to solve problems such as market failures or access to improved technologies. Meanwhile, for decades, collective action groups were considered as policy institutional tools to address these challenges and improve agricultural performance. However, despite the growing interest in these organizations in recent years, impact evaluations of the contribution of farmer organizations are still limited. This study, therefore, attempts to fill in the gap by providing more comprehensive insights on the role of farmer organizations, neighbourhood, and spatial heterogeneity in farm performances. Several methodological approaches were applied, and the main data used for empirical analyses come from a survey conducted in 2017 in Senegal which randomly sampled 4480 rain-fed cereal producing households. The dissertation is a collection of five essays. The first essay examines the empirical causal relationship between membership in farmer organizations and food availability. It applied a generalized spatial two-stage least squares method to control for selection biases and spatial heterogeneity. The results showed a positive and significant association between membership in farmer organizations and households' levels of food availability. The second essay analysed the impact of membership in farmer organizations on household land productivity and income. It applied the Endogenous Switching Regression model to derive treatment effects of membership in farmer organizations. The results showed positive, significant and heterogeneous effects of membership in farmer organizations. The third essay analyses the impact of membership in farmer organizations on rice farms technical efficiency. The essay combined the propensity score matching method with the sample selection stochastic frontier model and the stochastic meta-frontier approach, to mitigate selection biases in the sample and to account for technology heterogeneity. Findings mainly showed that members of farmer organizations do not perform better than non-members. The fourth essay explored the roles and complementarity of neighbourhood and membership in farmer organizations on the adoption of two productivity-enhancing technologies. After applying a Bayesian Spatial Durbin Probit model, the results reveal that close neighbouring farmers show similar choice behaviour regarding productivity-enhancing technologies, and membership in farmer organizations affects significantly and positively the choice of farmers and of their neighbours. The last essay aimed to provide empirical evidence on the Senegalese farmers' technical efficiency in the context of climate variability and spatial heterogeneity. Using simulated data, the paper first evaluated the newly developed spatial stochastic frontier estimation technique based on skew-normal distributions. Moreover, empirical findings reveal that farm technical efficiency appears to be significantly affected by unobserved spatial features. The findings of this dissertation induced some implications for policy and future research. First, support for farmer organizations in Senegal should take into account the spatial distribution of farmers. Second, policymakers when designing programs for rural areas should consider the social links created by both farmer organizations and farmers neighbourhoods. Third, policymakers should encourage more the design and dissemination of agricultural technologies that are very adaptable to specific spatial conditions of farmers. Finally, in the field of spatial stochastic frontier modelling, future studies should continue investigating the performances of the skew-normal approach

Increasing fMRI Sampling Rate Improves Granger Causality Estimates

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain