Experimental demonstration of a mu=-1 metamaterial lens for magnetic resonance imaging

In this work a mu=-1 metamaterial (MM) lens for magnetic resonance imaging (MRI) is demonstrated. MRI uses surface coils to detect the radiofrequency(RF) energy absorbed and emitted by the nuclear spins in the imaged object. The proposed MM lens manipulates the RF field detected by these surface coils, so that the coil sensitivity and spatial localization is substantially improved. Beyond this specific application, we feel that the reported results are the experimental confirmation of a new concept for the manipulation of RF field in MRI, which paves the way to many other interesting applications.Comment: 9 pages, 3 figure

Classification of pathology in diabetic eye disease

Proliferative diabetic retinopathy is a complication of diabetes that can eventually lead to blindness. Early identification of this complication reduces the risk of blindness by initiating timely treatment. We report the utility of pattern analysis tools linked with a simple linear discriminant analysis that not only identifies new vessel growth in the retinal fundus but also localises the area of pathology. Ten fluorescein images were analysed using seven feature descriptors including area, perimeter, circularity, curvature, entropy, wavelet second moment and the correlation dimension. Our results indicate that traditional features such as area or perimeter measures of neovascularisation associated with proliferative retinopathy were not sensitive enough to detect early proliferative retinopathy (SNR = 0.76, 0.75 respectively). The wavelet second moment provided the best discrimination with a SNR of 1.17. Combining second moment, curvature and global correlation dimension provided a 100% discrimination (SNR = 1)

On the resonances and polarizabilities of split ring resonators

In this paper, the behavior at resonance of split ring resonators SRRs and other related topologies, such as the nonbianisotropic SRR and the broadside-coupled SRR, are studied. It is shown that these structures exhibit a fundamental resonant mode the quasistatic resonance and other higher-order modes which are related to dynamic processes. The excitation of these modes by means of a properly polarized time varying magnetic and/or electric fields is discussed on the basis of resonator symmetries. To verify the electromagnetic properties of these resonators, simulations based on resonance excitation by nonuniform and uniform external fields have been performed. Inspection of the currents at resonances, inferred from particle symmetries and full-wave electromagnetic simulations, allows us to predict the first-order dipolar moments induced at the different resonators and to develop a classification of the resonances based on this concept. The experimental data, obtained in SRR-loaded waveguides, are in agreement with the theory and point out the rich phenomenology associated with these planar resonant structures.MEC (España)-TEC2004-04249-C02-01 y TEC2004-04249-C02-02Comunidad Europea (programa Eureka)-2895 TELEMACAgencia de Subvenciones de la República Checa-102/03/044

Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length

This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, λ. The reconstruction program takes as input, a set of correspondences between features in the model and features in the image. From this information, the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. This paper describes how the reconstruction problem can be framed as an optimization over a compact set with low dimension - no more than four. This optimization problem can be solved efficiently by coupling standard nonlinear optimization techniques with a multistart method which generates multiple starting points for the optimizer by sampling the parameter space uniformly. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated