The impact of spatial resolution and respiratory motion on MR imaging of atherosclerotic plaque.

PURPOSE: To examine the impact of spatial resolution and respiratory motion on the ability to accurately measure atherosclerotic plaque burden and to visually identify atherosclerotic plaque composition. MATERIALS AND METHODS: Numerical simulations of the Bloch equations and vessel wall phantom studies were performed for different spatial resolutions by incrementally increasing the field of view. In addition, respiratory motion was simulated based on a measured physiologic breathing pattern. RESULTS: While a spatial resolution of > or = 6 pixels across the wall does not result in significant errors, a resolution of < or = 4 pixels across the wall leads to an overestimation of > 20%. Using a double-inversion T2-weighted turbo spin echo sequence, a resolution of 1 pixel across equally thick tissue layers (fibrous cap, lipid, smooth muscle) and a respiratory motion correction precision (gating window) of three times the thickness of the tissue layer allow for characterization of the different coronary wall components. CONCLUSIONS: We found that measurements in low-resolution black blood images tend to overestimate vessel wall area and underestimate lumen area.

Study of OSEM with different subsets in grating-based X-ray differential phase-contrast imaging.

Impressive developments in X-ray imaging are associated with X-ray phase contrast computed tomography based on grating interferometry, a technique that provides increased contrast compared with conventional absorption-based imaging. A new "single-step" method capable of separating phase information from other contributions has been recently proposed. This approach not only simplifies data-acquisition procedures, but, compared with the existing phase step approach, significantly reduces the dose delivered to a sample. However, the image reconstruction procedure is more demanding than for traditional methods and new algorithms have to be developed to take advantage of the "single-step" method. In the work discussed in this paper, a fast iterative image reconstruction method named OSEM (ordered subsets expectation maximization) was applied to experimental data to evaluate its performance and range of applicability. The OSEM algorithm with different subsets was also characterized by comparison of reconstruction image quality and convergence speed. Computer simulations and experimental results confirm the reliability of this new algorithm for phase-contrast computed tomography applications. Compared with the traditional filtered back projection algorithm, in particular in the presence of a noisy acquisition, it furnishes better images at a higher spatial resolution and with lower noise. We emphasize that the method is highly compatible with future X-ray phase contrast imaging clinical applications.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

Image quality assessment in digital mammography: part II. NPWE as a validated alternative for contrast detail analysis.

Assessment of image quality for digital x-ray mammography systems used in European screening programs relies mainly on contrast-detail CDMAM phantom scoring and requires the acquisition and analysis of many images in order to reduce variability in threshold detectability. Part II of this study proposes an alternative method based on the detectability index (d') calculated for a non-prewhitened model observer with an eye filter (NPWE). The detectability index was calculated from the normalized noise power spectrum and image contrast, both measured from an image of a 5 cm poly(methyl methacrylate) phantom containing a 0.2 mm thick aluminium square, and the pre-sampling modulation transfer function. This was performed as a function of air kerma at the detector for 11 different digital mammography systems. These calculated d' values were compared against threshold gold thickness (T) results measured with the CDMAM test object and against derived theoretical relationships. A simple relationship was found between T and d', as a function of detector air kerma; a linear relationship was found between d' and contrast-to-noise ratio. The values of threshold thickness used to specify acceptable performance in the European Guidelines for 0.10 and 0.25 mm diameter discs were equivalent to threshold calculated detectability indices of 1.05 and 6.30, respectively. The NPWE method is a validated alternative to CDMAM scoring for use in the image quality specification, quality control and optimization of digital x-ray systems for screening mammography.

Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction.

Respiratory motion is a major source of artifacts in cardiac magnetic resonance imaging (MRI). Free-breathing techniques with pencil-beam navigators efficiently suppress respiratory motion and minimize the need for patient cooperation. However, the correlation between the measured navigator position and the actual position of the heart may be adversely affected by hysteretic effects, navigator position, and temporal delays between the navigators and the image acquisition. In addition, irregular breathing patterns during navigator-gated scanning may result in low scan efficiency and prolonged scan time. The purpose of this study was to develop and implement a self-navigated, free-breathing, whole-heart 3D coronary MRI technique that would overcome these shortcomings and improve the ease-of-use of coronary MRI. A signal synchronous with respiration was extracted directly from the echoes acquired for imaging, and the motion information was used for retrospective, rigid-body, through-plane motion correction. The images obtained from the self-navigated reconstruction were compared with the results from conventional, prospective, pencil-beam navigator tracking. Image quality was improved in phantom studies using self-navigation, while equivalent results were obtained with both techniques in preliminary in vivo studies.

Spatiotemporal adaptive multiscale multiphysics simulations of two-phase flow

We present a spatiotemporal adaptive multiscale algorithm, which is based on the Multiscale Finite Volume method. The algorithm offers a very efficient framework to deal with multiphysics problems and to couple regions with different spatial resolution. We employ the method to simulate two-phase flow through porous media. At the fine scale, we consider a pore-scale description of the flow based on the Volume Of Fluid method. In order to construct a global problem that describes the coarse-scale behavior, the equations are averaged numerically with respect to auxiliary control volumes, and a Darcy-like coarse-scale model is obtained. The space adaptivity is based on the idea that a fine-scale description is only required in the front region, whereas the resolution can be coarsened elsewhere. Temporal adaptivity relies on the fact that the fine-scale and the coarse-scale problems can be solved with different temporal resolution (longer time steps can be used at the coarse scale). By simulating drainage under unstable flow conditions, we show that the method is able to capture the coarse-scale behavior outside the front region and to reproduce complex fluid patterns in the front region.

Le Linceul: une exploration par la bande dessinée des potentialités narratives de la (re)production de l'image christique

L'article aborde la façon dont la série BD Le Linceul (2003-2006) s'approprie, dans le cadre de la fiction et du récit d'action grand public, le contexte de la réception de l'image acheiropoïète du Saint Suaire, intégrant la référence à des enjeux théologiques et scientifiques au sein d'une histoire à suspense située à différentes époques et dont les nombreux rebondissements s'articulent autour d'une croyance progressive dans le statut sacré de la vera icon. L'examen détaillé de certains choix narratifs et graphiques de l'auteur Laurent Bidot permet de dégager les modalités d'une exploitation contemporaine et (a priori) laïcisée de la figure de Jésus qui, à l'ère des technologies numériques, conserve sa part de mystère. Dans une perspective narratologique, l'interprétation procède principalement de constats liés à l'organisation énonciative de cette production bédéique.

Brain dysfunctions, psychopathologies, and body image distortions

The major features in eating disorders are a preoccupation with food and its consumption and body dissatisfaction. Diagnostic manuals provide clusters of criteria according to which affected individuals can be categorized into one or other group of eating disorder. Yet, when considering the high proportion of comorbidities and ignoring the content of the symptoms (food, body), the major features seem to yield obsessional-compulsive, addictive, and impulsive qualities. In the present article, we review studies from the neuroscientific literature (mainly lesion studies) on eating disorder, obsessive-compulsive disorder, impulse control disorder, and addiction to investigate the possibility of a wider phenotype that can be related to a common brain network. The literature localizes this network to the right frontal lobe and its connectivities. This network, when dysfunctional, might result in a behavior that favors the preoccupation with particular thoughts, behaviors, anxieties, and uncontrollable urges that are accompanied by little scope for ongoing behavioral adjustments (e.g., impulse control). We reason that this network may turn out to be equally involved in understudied mental conditions of dysfunctional body processing such as muscle dysmorphia, body dysmorphic disorder (including esthetic surgery), and xelomelia. We finally consider previous notions of a wider phenotype approach to current diagnostic practice (using DSM), such as the possibility of a model with a reduced number of diagnostic categories and primary and secondary factors, and to etiological models of mental health conditions.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Improved SNR efficiency in gradient echo coronary MRA with high temporal resolution using parallel imaging.

Contemporary coronary magnetic resonance angiography techniques suffer from signal-to-noise ratio (SNR) constraints. We propose a method to enhance SNR in gradient echo coronary magnetic resonance angiography by using sensitivity encoding (SENSE). While the use of sensitivity encoding to improve SNR seems counterintuitive, it can be exploited by reducing the number of radiofrequency excitations during the acquisition window while lowering the signal readout bandwidth, therefore improving the radiofrequency receive to radiofrequency transmit duty cycle. Under certain conditions, this leads to improved SNR. The use of sensitivity encoding for improved SNR in three-dimensional coronary magnetic resonance angiography is investigated using numerical simulations and an in vitro and an in vivo study. A maximum 55% SNR enhancement for coronary magnetic resonance angiography was found both in vitro and in vivo, which is well consistent with the numerical simulations. This method is most suitable for spoiled gradient echo coronary magnetic resonance angiography in which a high temporal and spatial resolution is required.

Occupational and patient exposure as well as image quality for full spine examinations with the EOS imaging system.

PURPOSE: EOS (EOS imaging S.A, Paris, France) is an x-ray imaging system that uses slot-scanning technology in order to optimize the trade-off between image quality and dose. The goal of this study was to characterize the EOS system in terms of occupational exposure, organ doses to patients as well as image quality for full spine examinations. METHODS: Occupational exposure was determined by measuring the ambient dose equivalents in the radiological room during a standard full spine examination. The patient dosimetry was performed using anthropomorphic phantoms representing an adolescent and a five-year-old child. The organ doses were measured with thermoluminescent detectors and then used to calculate effective doses. Patient exposure with EOS was then compared to dose levels reported for conventional radiological systems. Image quality was assessed in terms of spatial resolution and different noise contributions to evaluate the detector's performances of the system. The spatial-frequency signal transfer efficiency of the imaging system was quantified by the detective quantum efficiency (DQE). RESULTS: The use of a protective apron when the medical staff or parents have to stand near to the cubicle in the radiological room is recommended. The estimated effective dose to patients undergoing a full spine examination with the EOS system was 290μSv for an adult and 200 μSv for a child. MTF and NPS are nonisotropic, with higher values in the scanning direction; they are in addition energy-dependent, but scanning speed independent. The system was shown to be quantum-limited, with a maximum DQE of 13%. The relevance of the DQE for slot-scanning system has been addressed. CONCLUSIONS: As a summary, the estimated effective dose was 290μSv for an adult; the image quality remains comparable to conventional systems.