Patient exposure monitoring and radiation qualities in two-dimensional digital x-ray imaging

The methods for estimating patient exposure in x-ray imaging are based on the measurement of radiation incident on the patient. In digital imaging, the useful dose range of the detector is large and excessive doses may remain undetected. Therefore, real-time monitoring of radiation exposure is important. According to international recommendations, the measurement uncertainty should be lower than 7% (confidence level 95%). The kerma-area product (KAP) is a measurement quantity used for monitoring patient exposure to radiation. A field KAP meter is typically attached to an x-ray device, and it is important to recognize the effect of this measurement geometry on the response of the meter. In a tandem calibration method, introduced in this study, a field KAP meter is used in its clinical position and calibration is performed with a reference KAP meter. This method provides a practical way to calibrate field KAP meters. However, the reference KAP meters require comprehensive calibration. In the calibration laboratory it is recommended to use standard radiation qualities. These qualities do not entirely correspond to the large range of clinical radiation qualities. In this work, the energy dependence of the response of different KAP meter types was examined. According to our findings, the recommended accuracy in KAP measurements is difficult to achieve with conventional KAP meters because of their strong energy dependence. The energy dependence of the response of a novel large KAP meter was found out to be much lower than with a conventional KAP meter. The accuracy of the tandem method can be improved by using this meter type as a reference meter. A KAP meter cannot be used to determine the radiation exposure of patients in mammography, in which part of the radiation beam is always aimed directly at the detector without attenuation produced by the tissue. This work assessed whether pixel values from this detector area could be used to monitor the radiation beam incident on the patient. The results were congruent with the tube output calculation, which is the method generally used for this purpose. The recommended accuracy can be achieved with the studied method. New optimization of radiation qualities and dose level is needed when other detector types are introduced. In this work, the optimal selections were examined with one direct digital detector type. For this device, the use of radiation qualities with higher energies was recommended and appropriate image quality was achieved by increasing the low dose level of the system.

Optimizing a piezometric network in the estimation of the groundwater budget: a case study from a crystalline-rock watershed in southern India

An estimate of the groundwater budget at the catchment scale is extremely important for the sustainable management of available water resources. Water resources are generally subjected to over-exploitation for agricultural and domestic purposes in agrarian economies like India. The double water-table fluctuation method is a reliable method for calculating the water budget in semi-arid crystalline rock areas. Extensive measurements of water levels from a dense network before and after the monsoon rainfall were made in a 53 km(2)atershed in southern India and various components of the water balance were then calculated. Later, water level data underwent geostatistical analyses to determine the priority and/or redundancy of each measurement point using a cross-validation method. An optimal network evolved from these analyses. The network was then used in re-calculation of the water-balance components. It was established that such an optimized network provides far fewer measurement points without considerably changing the conclusions regarding groundwater budget. This exercise is helpful in reducing the time and expenditure involved in exhaustive piezometric surveys and also in determining the water budget for large watersheds (watersheds greater than 50 km(2)).

Methodological perspectives for register-based health system performance assessment : Developing a hip fracture monitoring system in Finland

The resources of health systems are limited. There is a need for information concerning the performance of the health system for the purposes of decision-making. This study is about utilization of administrative registers in the context of health system performance evaluation. In order to address this issue, a multidisciplinary methodological framework for register-based data analysis is defined. Because the fixed structure of register-based data indirectly determines constraints on the theoretical constructs, it is essential to elaborate the whole analytic process with respect to the data. The fundamental methodological concepts and theories are synthesized into a data sensitive approach which helps to understand and overcome the problems that are likely to be encountered during a register-based data analyzing process. A pragmatically useful health system performance monitoring should produce valid information about the volume of the problems, about the use of services and about the effectiveness of provided services. A conceptual model for hip fracture performance assessment is constructed and the validity of Finnish registers as a data source for the purposes of performance assessment of hip fracture treatment is confirmed. Solutions to several pragmatic problems related to the development of a register-based hip fracture incidence surveillance system are proposed. The monitoring of effectiveness of treatment is shown to be possible in terms of care episodes. Finally, an example on the justification of a more detailed performance indicator to be used in the profiling of providers is given. In conclusion, it is possible to produce useful and valid information on health system performance by using Finnish register-based data. However, that seems to be far more complicated than is typically assumed. The perspectives given in this study introduce a necessary basis for further work and help in the routine implementation of a hip fracture monitoring system in Finland.

Iron and manganese in groundwater in Finland : Occurrence in glacifluvial aquifers and removal by biofiltration

Yhteenveto: Rauta ja mangaani Suomen glasifluviaalisten akviferien pohjavedessä ja poisto biosuodatuksella

Geochemistry of fluoride rich groundwater in Kolar and Tumkur Districts of Karnataka

Groundwater is a significant water resource in India for domestic, irrigation, and industrial needs. By far the most serious natural groundwater-quality problem in India, in terms of public health, derives from high fluoride, arsenic, and iron concentrations. Hydrogeochemical investigation of fluoride contaminated groundwater samples from Kolar and Tumkur Districts in Karnataka are undertaken to understand the quality and potability of groundwater from the study area, the level of fluoride contamination, the origin and geochemical mechanisms driving the fluoride enrichment. Majority of the groundwater samples did not meet the potable water criteria as they contained excess (>1.5 mg/L) fluoride, dissolved salts (>500 mg/L) and total hardness (75-924 mg/L). Hydrogeochemical facies of the groundwater samples suggest that rock weathering and evaporation-crystallization control the groundwater composition in the study area with 50-67% of samples belonging to the Ca-HCO3 type and the remaining falling into the mixed Ca-Na-HCO3 or Ca-Mg-Cl type. The saturation index values indicated that the groundwater in the study area is oversaturated with respect to calcite and under-saturated with respect to fluorite. The deficiency of calcium ion concentration in the groundwater from calcite precipitation favors fluorite dissolution leading to excess fluoride concentration.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Development of a low-cost portable colorimeter for the estimation of fluoride in drinking water

People in many countries are affected by fluorosis owing to the high levels of fluoride in drinking water. An inexpensive method for estimating the concentration of the fluoride ion in drinking water would be helpful in identifying safe sources of water and also in monitoring the performance of defluoridation techniques. For this purpose, a simple, inexpensive, and portable colorimeter has been developed in the present work. It is used in conjunction with the SPADNS method, which shows a color change in the visible region on addition of water containing fluoride to a reagent solution. Groundwater samples were collected from different parts of the state of Karnataka, India and analysed for fluoride. The results obtained using the colorimeter and the double beam spectrophotometer agreed fairly well. The costs of the colorimeter and of the chemicals required per test were about Rs. 250 (US$ 5) and Rs. 2.5 (US$ 0.05), respectively. In addition, the cost of the chemicals required for constructing the calibration curve was about Rs. 15 (US$ 0.3). (C) 2010 Elsevier B.V. All rights reserved.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Mathematical modelling of groundwater pollution in a small heterogeneous aquifer at Kärkölä, southern Finland

Yhteenveto: Kärkölän likaantuneen pohjavesialueen matemaattinen mallinnus