Selective in vivo visualization of immune-cell infiltration in a mouse model of autoimmune myocarditis by fluorine-19 cardiac magnetic resonance.

BACKGROUND: The goal of this study was to characterize the performance of fluorine-19 ((19)F) cardiac magnetic resonance (CMR) for the specific detection of inflammatory cells in a mouse model of myocarditis. Intravenously administered perfluorocarbons are taken up by infiltrating inflammatory cells and can be detected by (19)F-CMR. (19)F-labeled cells should, therefore, generate an exclusive signal at the inflamed regions within the myocardium. METHODS AND RESULTS: Experimental autoimmune myocarditis was induced in BALB/c mice. After intravenous injection of 2×200 µL of a perfluorocarbon on day 19 and 20 (n=9) after immunization, in vivo (19)F-CMR was performed at the peak of myocardial inflammation (day 21). In 5 additional animals, perfluorocarbon combined with FITC (fluorescein isothiocyanate) was administered for postmortem immunofluorescence and flow-cytometry analyses. Control experiments were performed in 9 animals. In vivo (19)F-CMR detected myocardial inflammation in all experimental autoimmune myocarditis-positive animals. Its resolution was sufficient to identify even small inflammatory foci, that is, at the surface of the right ventricle. Postmortem immunohistochemistry and flow cytometry confirmed the presence of perfluorocarbon in macrophages, dendritic cells, and granulocytes, but not in lymphocytes. The myocardial volume of elevated (19)F signal (rs=0.96; P<0.001), the (19)F signal-to-noise ratio (rs=0.92; P<0.001), and the (19)F signal integral (rs=0.96; P<0.001) at day 21 correlated with the histological myocarditis severity score. CONCLUSIONS: In vivo (19)F-CMR was successfully used to visualize the inflammation specifically and robustly in experimental autoimmune myocarditis, and thus allowed for an unprecedented insight into the involvement of inflammatory cells in the disease process.

Model-based iterative reconstruction in pediatric chest CT: assessment of image quality in a prospective study of children with cystic fibrosis.

BACKGROUND: The potential effects of ionizing radiation are of particular concern in children. The model-based iterative reconstruction VEO(TM) is a technique commercialized to improve image quality and reduce noise compared with the filtered back-projection (FBP) method. OBJECTIVE: To evaluate the potential of VEO(TM) on diagnostic image quality and dose reduction in pediatric chest CT examinations. MATERIALS AND METHODS: Twenty children (mean 11.4 years) with cystic fibrosis underwent either a standard CT or a moderately reduced-dose CT plus a minimum-dose CT performed at 100 kVp. Reduced-dose CT examinations consisted of two consecutive acquisitions: one moderately reduced-dose CT with increased noise index (NI = 70) and one minimum-dose CT at CTDIvol 0.14 mGy. Standard CTs were reconstructed using the FBP method while low-dose CTs were reconstructed using FBP and VEO. Two senior radiologists evaluated diagnostic image quality independently by scoring anatomical structures using a four-point scale (1 = excellent, 2 = clear, 3 = diminished, 4 = non-diagnostic). Standard deviation (SD) and signal-to-noise ratio (SNR) were also computed. RESULTS: At moderately reduced doses, VEO images had significantly lower SD (P < 0.001) and higher SNR (P < 0.05) in comparison to filtered back-projection images. Further improvements were obtained at minimum-dose CT. The best diagnostic image quality was obtained with VEO at minimum-dose CT for the small structures (subpleural vessels and lung fissures) (P < 0.001). The potential for dose reduction was dependent on the diagnostic task because of the modification of the image texture produced by this reconstruction. CONCLUSIONS: At minimum-dose CT, VEO enables important dose reduction depending on the clinical indication and makes visible certain small structures that were not perceptible with filtered back-projection.

3D noise power spectrum applied on clinical mdct scanners: effects of reconstruction algorithms and reconstruction filters

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters.A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed.In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements.The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Mice from a genetically resistant background lacking the interferon gamma receptor are susceptible to infection with Leishmania major but mount a polarized T helper cell 1-type CD4+ T cell response.

Mice with homologous disruption of the gene coding for the ligand-binding chain of the interferon (IFN) gamma receptor and derived from a strain genetically resistant to infection with Leishmania major have been used to study further the role of this cytokine in the differentiation of functional CD4+ T cell subsets in vivo and resistance to infection. Wild-type 129/Sv/Ev mice are resistant to infection with this parasite, developing only small lesions, which resolve spontaneously within 6 wk. In contrast, mice lacking the IFN-gamma receptor develop large, progressing lesions. After infection, lymph nodes (LN) and spleens from both wild-type and knockout mice showed an expansion of CD4+ cells producing IFN-gamma as revealed by measuring IFN-gamma in supernatants of specifically stimulated CD4+ T cells, by enumerating IFN-gamma-producing T cells, and by Northern blot analysis of IFN-gamma transcripts. No biologically active interleukin (IL) 4 was detected in supernatants of in vitro-stimulated LN or spleen cells from infected wild-type or deficient mice. Reverse transcription polymerase chain reaction analysis with primers specific for IL-4 showed similar IL-4 message levels in LN from both types of mice. The IL-4 message levels observed were comparable to those found in similarly infected C57BL/6 mice and significantly lower than the levels found in BALB/c mice. Anti-IFN-gamma treatment of both types of mice failed to alter the pattern of cytokines produced after infection. These data show that even in the absence of IFN-gamma receptors, T helper cell (Th) 1-type responses still develop in genetically resistant mice with no evidence for the expansion of Th2 cells.

Three-Dimensional (3D) Microarchitecture Correlations with 2D Projection Image Gray-Level Variations Assessed by Trabecular Bone Score Using High-Resolution Computed Tomographic Acquisitions: Effects of Resolution and Noise.

The aim of the present study is to determine the level of correlation between the 3-dimensional (3D) characteristics of trabecular bone microarchitecture, as evaluated using microcomputed tomography (μCT) reconstruction, and trabecular bone score (TBS), as evaluated using 2D projection images directly derived from 3D μCT reconstruction (TBSμCT). Moreover, we have evaluated the effects of image degradation (resolution and noise) and X-ray energy of projection on these correlations. Thirty human cadaveric vertebrae were acquired on a microscanner at an isotropic resolution of 93μm. The 3D microarchitecture parameters were obtained using MicroView (GE Healthcare, Wauwatosa, MI). The 2D projections of these 3D models were generated using the Beer-Lambert law at different X-ray energies. Degradation of image resolution was simulated (from 93 to 1488μm). Relationships between 3D microarchitecture parameters and TBSμCT at different resolutions were evaluated using linear regression analysis. Significant correlations were observed between TBSμCT and 3D microarchitecture parameters, regardless of the resolution. Correlations were detected that were strongly to intermediately positive for connectivity density (0.711≤r(2)≤0.752) and trabecular number (0.584≤r(2)≤0.648) and negative for trabecular space (-0.407 ≤r(2)≤-0.491), up to a pixel size of 1023μm. In addition, TBSμCT values were strongly correlated between each other (0.77≤r(2)≤0.96). Study results show that the correlations between TBSμCT at 93μm and 3D microarchitecture parameters are weakly impacted by the degradation of image resolution and the presence of noise.

Contribution of genetic background, traditional risk factors, and HIV-related factors to coronary artery disease events in HIV-positive persons.

BACKGROUND: Persons infected with human immunodeficiency virus (HIV) have increased rates of coronary artery disease (CAD). The relative contribution of genetic background, HIV-related factors, antiretroviral medications, and traditional risk factors to CAD has not been fully evaluated in the setting of HIV infection. METHODS: In the general population, 23 common single-nucleotide polymorphisms (SNPs) were shown to be associated with CAD through genome-wide association analysis. Using the Metabochip, we genotyped 1875 HIV-positive, white individuals enrolled in 24 HIV observational studies, including 571 participants with a first CAD event during the 9-year study period and 1304 controls matched on sex and cohort. RESULTS: A genetic risk score built from 23 CAD-associated SNPs contributed significantly to CAD (P = 2.9 × 10(-4)). In the final multivariable model, participants with an unfavorable genetic background (top genetic score quartile) had a CAD odds ratio (OR) of 1.47 (95% confidence interval [CI], 1.05-2.04). This effect was similar to hypertension (OR = 1.36; 95% CI, 1.06-1.73), hypercholesterolemia (OR = 1.51; 95% CI, 1.16-1.96), diabetes (OR = 1.66; 95% CI, 1.10-2.49), ≥ 1 year lopinavir exposure (OR = 1.36; 95% CI, 1.06-1.73), and current abacavir treatment (OR = 1.56; 95% CI, 1.17-2.07). The effect of the genetic risk score was additive to the effect of nongenetic CAD risk factors, and did not change after adjustment for family history of CAD. CONCLUSIONS: In the setting of HIV infection, the effect of an unfavorable genetic background was similar to traditional CAD risk factors and certain adverse antiretroviral exposures. Genetic testing may provide prognostic information complementary to family history of CAD.

Comparaison objective de systèmes d'imagerie conventionnelle et numérique en mammographie

L'objectif de ce travail est le développement d'une méthode de caractérisation objective de la qualité d'image s'appliquant à des systèmes de mammographie analogique, utilisant un couple écran-film comme détecteur, et numérique, basé sur une technologie semi-conductrice, ceci en vue de la comparaison de leurs performances. La méthode développée tient compte de la gamme dynamique du détecteur, de la détectabilité de structures de haut contraste, simulant des microcalcifications, et de structures de bas contraste, simulant des opacités (nodules tumoraux). La méthode prend également en considération le processus de visualisation de l'image, ainsi que la réponse de l'observateur. Pour réaliser ceci, un objet-test ayant des propriétés proches de celles d'un sein comprimé, composé de différents matériaux équivalents aux tissus, allant du glandulaire à l'adipeux, et comprenant des zones permettant la simulation de structures de haut et bas contraste, ainsi que la mesure de la résolution et celle du bruit, a été développé et testé. L'intégration du processus de visualisation a été réalisée en utilisant une caméra CCD mesurant directement les paramètres de qualité d'image, à partir de l'image de l'objet-test, dans une grandeur physique commune au système numérique et analogique, à savoir la luminance arrivant sur l'oeil de l'observateur. L'utilisation d'une grandeur synthétique intégrant dans un même temps, le contraste, le bruit et la résolution rend possible une comparaison objective entre les deux systèmes de mammographie. Un modèle mathématique, simulant la réponse d'un observateur et intégrant les paramètres de base de qualité d'image, a été utilisé pour calculer la détectabilité de structures de haut et bas contraste en fonction du type de tissu sur lequel celles-ci se trouvent. Les résultats obtenus montrent qu'à dose égale la détectabilité des structures est significativement plus élevée avec le système de mammographie numérique qu'avec le système analogique. Ceci est principalement lié au fait que le bruit du système numérique est plus faible que celui du système analogique. Les résultats montrent également que la méthodologie, visant à comparer des systèmes d'imagerie numérique et analogique en utilisant un objet-test à large gamme dynamique ainsi qu'une caméra, peut être appliquée à d'autres modalités radiologiques, ainsi qu'à une démarche d'optimisation des conditions de lecture des images.<br/><br/>The goal of this work was to develop a method to objectively compare the performance of a digital and a screen-film mammography system in terms of image quality and patient dose. We propose a method that takes into account the dynamic range of the image detector and the detection of high contrast (for microcalcifications) and low contrast (for masses or tumoral nodules) structures. The method also addresses the problems of image visualization and the observer response. A test object, designed to represent a compressed breast, was constructed from various tissue equivalent materials ranging from purely adipose to purely glandular composition. Different areas within the test object permitted the evaluation of low and high contrast detection, spatial resolution, and image noise. All the images (digital and conventional) were captured using a CCD camera to include the visualization process in the image quality assessment. In this way the luminance reaching the viewer?s eyes can be controlled for both kinds of images. A global quantity describing image contrast, spatial resolution and noise, and expressed in terms of luminance at the camera, can then be used to compare the two technologies objectively. The quantity used was a mathematical model observer that calculates the detectability of high and low contrast structures as a function of the background tissue. Our results show that for a given patient dose, the detection of high and low contrast structures is significantly better for the digital system than for the conventional screen-film system studied. This is mainly because the image noise is lower for the digital system than for the screen-film detector. The method of using a test object with a large dynamic range combined with a camera to compare conventional and digital imaging modalities can be applied to other radiological imaging techniques. In particular it could be used to optimize the process of radiographic film reading.

Tolerance and Relative Utility: Two Proposed Indices for Comparing Change in Clinical Measurement Noise Between Different Populations (Repeatability) or Measurement Methods (Agreement).

Reaching a consensus in terms of interchangeability and utility (i.e., disease detection/monitoring) of a medical device is the eventual aim of repeatability and agreement studies. The aim of the tolerance and relative utility indices described in this report is to provide a methodology to compare change in clinical measurement noise between different populations (repeatability) or measurement methods (agreement), so as to highlight problematic areas. No longitudinal data are required to calculate these indices. Both indices establish a metric of least to most effected across all parameters to facilitate comparison. If validated, these indices may prove useful tools when combining reports and forming the consensus required in the validation process for software updates and new medical devices.

Physiological noise in human cerebellar fMRI.

OBJECTIVES: To compare physiological noise contributions in cerebellar and cerebral regions of interest in high-resolution functional magnetic resonance imaging (fMRI) data acquired at 7T, to estimate the need for physiological noise removal in cerebellar fMRI. MATERIALS AND METHODS: Signal fluctuations in high resolution (1 mm isotropic) 7T fMRI data were attributed to one of the following categories: task-induced BOLD changes, slow drift, signal changes correlated with the cardiac and respiratory cycles, signal changes related to the cardiac rate and respiratory volume per unit of time or other. [Formula: see text] values for all categories were compared across regions of interest. RESULTS: In this high-resolution data, signal fluctuations related to the phase of the cardiac cycle and cardiac rate were shown to be significant, but comparable between cerebellar and cerebral regions of interest. However, respiratory related signal fluctuations were increased in the cerebellar regions, with explained variances that were up to 80 % higher than for the primary motor cortex region. CONCLUSION: Even at a millimetre spatial resolution, significant correlations with both cardiac and respiratory RETROICOR components were found in all healthy volunteer data. Therefore, physiological noise correction is highly likely to improve the temporal signal-to-noise ratio (SNR) for cerebellar fMRI at 7T, even at high spatial resolution.

A comprehensive model for quantum noise characterization in digital mammography.

NlmCategory="UNASSIGNED">A version of cascaded systems analysis was developed specifically with the aim of studying quantum noise propagation in x-ray detectors. Signal and quantum noise propagation was then modelled in four types of x-ray detectors used for digital mammography: four flat panel systems, one computed radiography and one slot-scan silicon wafer based photon counting device. As required inputs to the model, the two dimensional (2D) modulation transfer function (MTF), noise power spectra (NPS) and detective quantum efficiency (DQE) were measured for six mammography systems that utilized these different detectors. A new method to reconstruct anisotropic 2D presampling MTF matrices from 1D radial MTFs measured along different angular directions across the detector is described; an image of a sharp, circular disc was used for this purpose. The effective pixel fill factor for the FP systems was determined from the axial 1D presampling MTFs measured with a square sharp edge along the two orthogonal directions of the pixel lattice. Expectation MTFs were then calculated by averaging the radial MTFs over all possible phases and the 2D EMTF formed with the same reconstruction technique used for the 2D presampling MTF. The quantum NPS was then established by noise decomposition from homogenous images acquired as a function of detector air kerma. This was further decomposed into the correlated and uncorrelated quantum components by fitting the radially averaged quantum NPS with the radially averaged EMTF(2). This whole procedure allowed a detailed analysis of the influence of aliasing, signal and noise decorrelation, x-ray capture efficiency and global secondary gain on NPS and detector DQE. The influence of noise statistics, pixel fill factor and additional electronic and fixed pattern noises on the DQE was also studied. The 2D cascaded model and decompositions performed on the acquired images also enlightened the observed quantum NPS and DQE anisotropy.