2 collectivity in shell-model calculations for odd-mass nuclei near

Shell-model calculations for 127,129In and 129,131Sb are presented, and interpreted in the context of the particle-core coupling scheme, wherein proton g9/2 holes or g7/2 particles are added to semimagic 128,130Sn cores. These results indicate that the particle-core coupling scheme is appropriate for the Sb isotopes, whilst less so for the In isotopes. B(E2) excitation strengths are also calculated, and show evidence of enhanced collectivity in both Sb isotopes, especially 131Sb. This observation suggests that 131Sb would be an excellent case for an experimental study seeking to investigate the early onset of collectivity near 132Sn

Studying the relative impact of ghosting and noise on the perceived quality of MR images

In current magnetic resonance (MR) imaging systems, design choices are confronted with a trade-off between structured (i.e. artifacts) and unstructured noise. The impact of both types of noise on perceived image quality, however, is so far unknown, while this knowledge would be highly beneficial for further improvement of MR imaging systems. In this paper, we investigate how ghosting artifacts (i.e. structured noise) and random noise, applied at the same energy level in the distortion, affect the perceived quality of MR images. To this end, a perception experiment is conducted with human observers rating the quality of a set of images, distorted with various levels of ghosting and noise. To also understand the influence of professional expertise on the image quality assessment task, two groups of observers with different levels of medical imaging experience participated in the experiment: one group contained fifteen clinical scientists or application specialists, and the other group contained eighteen naïve observers. Experimental results indicate that experts and naïve observers differently assess the quality of MR images degraded with ghosting/noise. Naïve observers consistently rate images degraded with ghosting higher than images degraded with noise, independent of the energy level of the distortion, and of the image content. For experts, the relative impact of ghosting and noise on perceived quality tends to depend on the energy level of the distortion and on the image content, but overall the energy of the distortion is a promising metric to predict perceived image qualityIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

E2 collectivity in shell-model calculations for odd-mass nuclei near 132Sn

Shell-model calculations for 127,129In and 129,131Sb are presented, and interpreted in the context of the particle-core coupling scheme, wherein proton g9/2 holes or g7/2 particles are added to semimagic 128,130Sn cores. These results indicate that the particle-core coupling scheme is appropriate for the Sb isotopes, whilst less so for the In isotopes. B(E2) excitation strengths are also calculated, and show evidence of enhanced collectivity in both Sb isotopes, especially 131Sb. This observation suggests that 131Sb would be an excellent case for an experimental study seeking to investigate the early onset of collectivity near 132Sn

Spiral MRI Scan-Time Reduction through Omission of Interleaves and Bayesian Image Reconstruction

Introduction Spiral scan-time can be reduced by omitting interleaves. However, the associated undersampling of k-space causes severe ringing artefacts in the reconstructed image. In [1] we reported on an iterative method, that reduces the image artefacts by estimating the omitted interleaves prior to image reconstruction. We have now developed a Bayesian reconstruction algorithm [2], capable of directly estimating the image given the sparse spiral data-set. The algorithm, that obviates gridding and density correction, amounts to explicit maximization of a posterior [2]. Any available optimization method can carry out this maximization. The new algorithm allows a sparser sampling of k-space and therefore a larger scan-time reduction than the method reported in [1]. Method We have developed a Bayesian reconstruction algorithm [2] enabling reconstruction of images from an arbitrarily and sparsely sampled k-space.

Improved Kaiser-Bessel Window Parameter Selection for Gridding

Introduction The gridding algorithm is frequently used to reconstruct an image from a non-uniformly sampled k-space. An important part of the algorithm is a convolution with a window. Usually, the Kaiser-Bessel window is used [1], which has a width L and a parameter B. A criterion for selecting an optimal value for B is the relative amount of aliased energy (including roll-off correction) of the window as used by Jackson [2]. In this abstract we show that the values for B found in [2] are not always optimal and introduce a method to find improved values for B given the sampling distribution. Method Resampling the convolved samples to a Cartesian grid in k- space causes aliasing. We write the aliased energy as: Z jxj?FOV fi fi fi fi c(x) \Delt