We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R Key words: compressed sensing; q-space; diffusion spectrum imaging; kurtosis; undersampling; orientation distribution function Over the last decade the application of diffusionweighted MR imaging to the central nervous system has gained significant attention. Recently, Inglese and Bester (1) reviewed the importance of diffusion in clinical evaluation of multiple sclerosis. Similarly, earlier studies indicated that diffusion tensor imaging could be used to detect evidence of traumatic brain injury (2). Diffusion tensor imaging samples only a very small subset of the full diffusion information encoded in q-space and describes diffusion as single compartment gaussian (3). This assumption however falls short for instance in fiber crossings or in biological tissue (4), which may exhibit restricted, non-gaussian diffusion. The concept of full qspace imaging to study molecular diffusion and tissue microstructure was introduced by Callaghan et al. (5) and first applied to brain tissue by King et al. (6); its modulus Fourier transform variant using finite gradient pulse widths is known as diffusion spectrum MR imaging (DSI) (7). DSI samples the full q-space and can be related to a center-of-mass weighted displacement space (8) by Fourier transform. Despite the large information content of DSI, its high dimensionality (three dimensions in the spatial domain [k-space] and three dimensions in the q-space) leading to very long acquisition times, severely limited its clinical application in vivo. And indeed the application of DSI has been reported only a few times in biological systems (6,9), although the nonlocalized analysis of q-space is commonly used in porous media (10). It can however be envisioned that using the full potential of diffusion information of full q-space to derive and evaluate surrogate markers for multiple sclerosis (MS) and traumatic brain injury would add significant clinical benefit and indeed more extended sampling of diffusion...

The quasiparticle spectrum of a two-dimensional d-wave superconductor in the mixed state, Hc1 ≪ H ≪ Hc2, is studied both analytically and numerically using the linearized Bogoliubov-de Gennes equation. We consider various values of the "anisotropy ratio" vF /v∆ for the quasiparticle velocities at the Dirac points, and we examine the implications of symmetry. For a Bravais lattice of vortices, we find there is always an isolated energy-zero (Dirac point) at the center of the Brillouin zone, but for a non-Bravais lattice with two vortices per unit cell there is generally an energy gap. In both of these cases, the density of states should vanish at zero energy, in contrast with the semiclassical prediction of a constant density of states, though the latter may hold down to very low energies for large anisotropy ratios. This result is closely related to the particle-hole symmetry of the band structures in lattices with two vortices per unit cell. More complicated non-Bravais vortex lattice configurations with at least four vortices per unit cell can break the particle-hole symmetry of the linearized energy spectrum and lead to a finite density of states at zero energy.

GNC in DWI improved spatial accuracy and interscanner reproducibility of ADC.

Purpose: To determine whether the promise of high-density many-coil MRI receiver arrays for enabling highly accelerated parallel imaging can be realized in practice. Materials and Methods:A 128-channel body receiver-coil array and custom MRI system were developed. The array comprises two clamshells containing 64 coils each, with the posterior array built to maximize signal-to-noise ratio (SNR) and the anterior array design incorporating considerations of weight and flexibility as well. Phantom imaging and human body imaging were performed using a variety of reduction factors and 2D and 3D pulse sequences. Results:The ratio of SNR relative to a 32-element array of similar footprint was 1.03 in the center of an elliptical loading phantom and 1.7 on average in the outer regions. Maximum g-factors dropped from 5.5 (for 32 channels) to 2.0 (for 128 channels) for 4 ϫ 4 acceleration and from 25 to 3.3 for 5 ϫ 5 acceleration. Residual aliasing artifacts for a right/left (R/L) reduction factor of 8 in human body imaging were significantly reduced relative to the 32-channel array. Conclusion:MRI with a large number of receiver channels enables significantly higher acceleration factors for parallel imaging and improved SNR, provided losses from the coils and electronics are kept negligible.

We apply a promising new method from the field of representations of Lie groups to calculate integrals over unitary groups, which are important for multi-antenna communications. To demonstrate the power and simplicity of this technique, we first re-derive a number of results that have been used recently in the community of wireless information theory, using only a few simple steps. In particular, we derive the joint probability distribution of eigenvalues of the matrix GG † , with G a semicorrelated Gaussian random matrix or a Gaussian random matrix with a non-zero mean. These joint probability distribution functions can then be used to calculate the moment generating function of the mutual information for Gaussian MIMO channels with these probability distribution of their channel matrices G. We then turn to the previously unsolved problem of calculating the moment generating function of the mutual information of MIMO channels, which are correlated at both the receiver and the transmitter. From this moment generating function we obtain the ergodic average of the mutual information and study the outage probability. These methods can be applied to a number of other problems. As a particular example, we examine unitary encoded space-time transmission of MIMO systems and we derive the received signal distribution when the channel matrix is correlated at the transmitter end.