Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS).

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

[Feasibilities and limitations of high field parallel MRI]. / Möglichkeiten und Grenzen der parallelen MRT im Hochfeld.

In medical magnetic resonance imaging (MRI) it is standard to use MR scanners with a field strength of 1.5 Tesla. Recently, an ongoing development to higher field strength can be observed and a new clinical standard at 3.0 Tesla seems to be established. High field MRI with its intrinsic higher signal to noise ratio (SNR) can enable new applications of MRI in medical diagnosis, or can serve to improve existing methods. It is important to note, that the use of high field MRI is not without its limitations. Besides the SNR, other unwanted effects increase with a higher field strength. Without correction, these high field problems cause a serious loss in image quality. An elegant way to address these problems is the use of parallel imaging. In many clinical applications, parallel MRI (pMRI) is part of the standard protocol, because pMRI can enhance virtually every MRI application, without necessarily affecting the contrast behavior of the underlying imaging sequence. In high field MRI, besides the speed advantage of pMRI, the positive influence on high field specific problems and therefore on the image quality will be of major importance.

Resolution enhancement in lung 1H imaging using parallel imaging methods.

Resolution in (1)H lung imaging is limited mainly by the acquisition time. Today, half-Fourier acquisition single-shot turbo spin-echo (HASTE) sequences, with short echo time (TE) and short interecho spacing (T(inter)) have found increased use in lung imaging. In this study, a HASTE sequence was used in combination with a partially parallel acquisition (PPA) strategy to increase the spatial resolution in single-shot (1)H lung imaging. To investigate the benefits of using a combination of single-shot sequences and PPA, five healthy volunteers were examined. Compared to conventional imaging methods, substantially increased resolution is obtained using the PPA approach. Representative in vivo (1)H lung images acquired with a HASTE sequence in combination with the generalized autocalibrating partially parallel acquisition (GRAPPA) method, up to an acceleration factor of three, are presented.

VD-AUTO-SMASH imaging.

Recently a self-calibrating SMASH technique, AUTO-SMASH, was described. This technique is based on PPA with RF coil arrays using auto-calibration signals. In AUTO-SMASH, important coil sensitivity information required for successful SMASH reconstruction is obtained during the actual scan using the correlation between undersampled SMASH signal data and additionally sampled calibration signals with appropriate offsets in k-space. However, AUTO-SMASH is susceptible to noise in the acquired data and to imperfect spatial harmonic generation in the underlying coil array. In this work, a new modified type of internal sensitivity calibration, VD-AUTO-SMASH, is proposed. This method uses a VD k-space sampling approach and shows the ability to improve the image quality without significantly increasing the total scan time. This new k-space adapted calibration approach is based on a k-space-dependent density function. In this scheme, fully sampled low-spatial frequency data are acquired up to a given cutoff-spatial frequency. Above this frequency, only sparse SMASH-type sampling is performed. On top of the VD approach, advanced fitting routines, which allow an improved extraction of coil-weighting factors in the presence of noise, are proposed. It is shown in simulations and in vivo cardiac images that the VD approach significantly increases the potential and flexibility of rapid imaging with AUTO-SMASH.