Characterization of Superparamagnetic Iron Oxide Coated with Silicone Used as Contrast Agent for Magnetic Resonance Image for the Gastrointestinal Tract

The present work is a report of the characterization of superparamagnetic iron oxide nanoparticles coated with silicone used as a contrast agent in magnetic resonance imaging of the gastrointestinal tract. The hydrodynamic size of the contrast agent is 281.2 rim, where it was determined by transmission electron microscopy and a Fe(3)O(4) crystalline structure was identified by X-ray diffraction, also confirmed by Mossbauer Spectroscopy. The blocking temperature of 190 K was determined from magnetic measurements based on the Zero Field Cooled and Field Cooled methods. The hysteresis loops were measured at different temperatures below and above the blocking temperature. Ferromagnetic resonance analysis indicated the superparamagnetic nature of the nanoparticles and a strong temperature dependence of the peak-to-peak linewidth Delta H(pp), giromagnetic factor g, number of spins N(S) and relaxation time T(2) were observed. This behavior can be attributed to an increase in the superexchange interaction.

Application of a wrist dosimeter prototype for radiation monitoring ((153)Sm) during a therapeutic procedure simulation

Gamma and beta radiation emitting radiopharmaceuticals are handled in nuclear medicine services, and in many cases there is only individual monitoring of gamma radiation. In this paper, the results obtained using a wrist dosimeter prototype (CaSO(4):Dy + Teflon pellets) show that the doses for workers occupationally exposed to beta radiation from (153)Sm are not negligible. It is important that this dose is evaluated, and it has to be taken into consideration in the individual monitoring system.

Improving wheat simulation capabilities in Australia from a cropping systems perspective: water and nitrogen effects on spring wheat in a semi-arid environment

Systems approaches can help to evaluate and improve the agronomic and economic viability of nitrogen application in the frequently water-limited environments. This requires a sound understanding of crop physiological processes and well tested simulation models. Thus, this experiment on spring wheat aimed to better quantify water x nitrogen effects on wheat by deriving some key crop physiological parameters that have proven useful in simulating crop growth. For spring wheat grown in Northern Australia under four levels of nitrogen (0 to 360 kg N ha(-1)) and either entirely on stored soil moisture or under full irrigation, kernel yields ranged from 343 to 719 g m(-2). Yield increases were strongly associated with increases in kernel number (9150-19950 kernels m(-2)), indicating the sensitivity of this parameter to water and N availability. Total water extraction under a rain shelter was 240 mm with a maximum extraction depth of 1.5 m. A substantial amount of mineral nitrogen available deep in the profile (below 0.9 m) was taken up by the crop. This was the source of nitrogen uptake observed after anthesis. Under dry conditions this late uptake accounted for approximately 50% of total nitrogen uptake and resulted in high (>2%) kernel nitrogen percentages even when no nitrogen was applied,Anthesis LAI values under sub-optimal water supply were reduced by 63% and under sub-optimal nitrogen supply by 50%. Radiation use efficiency (RUE) based on total incident short-wave radiation was 1.34 g MJ(-1) and did not differ among treatments. The conservative nature of RUE was the result of the crop reducing leaf area rather than leaf nitrogen content (which would have affected photosynthetic activity) under these moderate levels of nitrogen limitation. The transpiration efficiency coefficient was also conservative and averaged 4.7 Pa in the dry treatments. Kernel nitrogen percentage varied from 2.08 to 2.42%. The study provides a data set and a basis to consider ways to improve simulation capabilities of water and nitrogen effects on spring wheat. (C) 1997 Elsevier Science B.V.

Patient Characteristics as Predictors of Image Quality and Diagnostic Accuracy of MDCT Compared With Conventional Coronary Angiography for Detecting Coronary Artery Stenoses: CORE-64 Multicenter International Trial

OBJECTIVE. The purpose of the study was to investigate patient characteristics associated with image quality and their impact on the diagnostic accuracy of MDCT for the detection of coronary artery stenosis. MATERIALS AND METHODS. Two hundred ninety-one patients with a coronary artery calcification (CAC) score of <= 600 Agatston units (214 men and 77 women; mean age, 59.3 +/- 10.0 years [SD]) were analyzed. An overall image quality score was derived using an ordinal scale. The accuracy of quantitative MDCT to detect significant (>= 50%) stenoses was assessed using quantitative coronary angiography (QCA) per patient and per vessel using a modified 19-segment model. The effect of CAC, obesity, heart rate, and heart rate variability on image quality and accuracy were evaluated by multiple logistic regression. Image quality and accuracy were further analyzed in subgroups of significant predictor variables. Diagnostic analysis was determined for image quality strata using receiver operating characteristic (ROC) curves. RESULTS. Increasing body mass index (BMI) (odds ratio [OR] = 0.89, p < 0.001), increasing heart rate (OR = 0.90, p < 0.001), and the presence of breathing artifact (OR = 4.97, p = 0.001) were associated with poorer image quality whereas sex, CAC score, and heart rate variability were not. Compared with examinations of white patients, studies of black patients had significantly poorer image quality (OR = 0.58, p = 0.04). At a vessel level, CAC score (10 Agatston units) (OR = 1.03, p = 0.012) and patient age (OR = 1.02, p = 0.04) were significantly associated with the diagnostic accuracy of quantitative MDCT compared with QCA. A trend was observed in differences in the areas under the ROC curves across image quality strata at the vessel level (p = 0.08). CONCLUSION. Image quality is significantly associated with patient ethnicity, BMI, mean scan heart rate, and the presence of breathing artifact but not with CAC score at a patient level. At a vessel level, CAC score and age were associated with reduced diagnostic accuracy.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.