Felix A. Breuer

SMASH, SENSE, PILS, GRAPPA How to Choose the Optimal Method

Fast imaging methods and the availability of required hardware for magnetic resonance tomography (MRT) have significantly reduced acquisition times from about an hour down to several minutes or seconds. With this development over the last 20 years, magnetic resonance imaging (MRI) has become one of the most important instruments in clinical diagnosis. In recent years, the greatest progress in further increasing imaging speed has been the development of parallel MRI (pMRI). Within the last 3 years, parallel imaging methods have become commercially available, and therefore are now available for a broad clinical use. The basic feature of pMRI is a scan time reduction, applicable to nearly any available MRI method, while maintaining the contrast behavior without requiring higher gradient system performance. Because of its faster image acquisition, pMRI can in some cases even significantly improve image quality. In the last 10 years of pMRI development, several different pMRI reconstruction methods have been set up which partially differ in their philosophy, in the mode of reconstruction as well in their advantages and drawbacks with regard to a successful image reconstruction. In this review, a brief overview is given on the advantages and disadvantages of present pMRI methods in clinical applications, and examples from different daily clinical applications are shown.

Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi-slice imaging.

In all current parallel imaging techniques, aliasing artifacts resulting from an undersampled acquisition are removed by means of a specialized image reconstruction algorithm. In this study a new approach termed “controlled aliasing in parallel imaging results in higher acceleration” (CAIPIRINHA) is presented. This technique modifies the appearance of aliasing artifacts during the acquisition to improve the subsequent parallel image reconstruction procedure. This new parallel multi‐slice technique is more efficient compared to other multi‐slice parallel imaging concepts that use only a pure postprocessing approach. In this new approach, multiple slices of arbitrary thickness and distance are excited simultaneously with the use of multi‐band radiofrequency (RF) pulses similar to Hadamard pulses. These data are then undersampled, yielding superimposed slices that appear shifted with respect to each other. The shift of the aliased slices is controlled by modulating the phase of the individual slices in the multi‐band excitation pulse from echo to echo. We show that the reconstruction quality of the aliased slices is better using this shift. This may potentially allow one to use higher acceleration factors than are used in techniques without this excitation scheme. Additionally, slices that have essentially the same coil sensitivity profiles can be separated with this technique. Magn Reson Med 53:684–691, 2005.

Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA).

Current parallel imaging techniques for accelerated imaging require a fully encoded reference data set to estimate the spatial coil sensitivity information needed for reconstruction. In dynamic parallel imaging a time‐interleaved acquisition scheme can be used, which eliminates the need for separately acquiring additional reference data, since the signal from directly adjacent time frames can be merged to build a set of fully encoded full‐resolution reference data for coil calibration. In this work, we demonstrate that a time‐interleaved sampling scheme, in combination with autocalibrated GRAPPA (referred to as TGRAPPA), allows one to easily update the coil weights for the GRAPPA algorithm dynamically, thereby improving the acquisition efficiency. This method may update coil sensitivity estimates frame by frame, thereby tracking changes in relative coil sensitivities that may occur during the data acquisition. Magn Reson Med 53:981–985, 2005. Published 2005 Wiley‐Liss, Inc.

Controlled Aliasing in Volumetric Parallel Imaging (2D CAIPIRINHA)

The CAIPIRINHA (Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration) concept in parallel imaging has recently been introduced, which modifies the appearance of aliasing artifacts during data acquisition in order to improve the subsequent parallel imaging reconstruction procedure. This concept has been successfully applied to simultaneous multi‐slice imaging (MS CAIPIRINHA). In this work, we demonstrate that the concept of CAIPIRINHA can also be transferred to 3D imaging, where data reduction can be performed in two spatial dimensions simultaneously. In MS CAIPIRINHA, aliasing is controlled by providing individual slices with different phase cycles by means of alternating multi‐band radio frequency (RF) pulses. In contrast to MS CAIPIRINHA, 2D CAIPIRINHA does not require special RF pulses. Instead, aliasing in 2D parallel imaging can be controlled by modifying the phase encoding sampling strategy. This is done by shifting sampling positions from their normal positions in the undersampled 2D phase encoding scheme. Using this modified sampling strategy, coil sensitivity variations can be exploited more efficiently in multiple dimensions, resulting in a more robust parallel imaging reconstruction. Magn Reson Med, 2006.

General formulation for quantitative G-factor calculation in GRAPPA reconstructions.

In this work a theoretical description for practical quantitative estimation of the noise enhancement in generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstructions, equivalent to the geometry (g)‐factor in sensitivity encoding for fast MRI (SENSE) reconstructions, is described. The GRAPPA g‐factor is derived directly from the GRAPPA reconstruction weights. The procedure presented here also allows the calculation of quantitative g‐factor maps for both the uncombined and combined accelerated GRAPPA images. This enables, for example, a fast comparison between the performances of various GRAPPA reconstruction kernels or SENSE reconstructions. The applicability of this approach is validated on phantom studies and demonstrated using in vivo images for 1D and 2D parallel imaging. Magn Reson Med, 2009.

Neural response to reward anticipation is modulated by Gray's impulsivity.

According to the Reinforcement Sensitivity Theory (RST), Grays dimension of impulsivity, reflecting human trait reward sensitivity, determines the extent to which stimuli activate the Behavioural Approach System (BAS). The potential neural underpinnings of the BAS, however, remain poorly understood. In the present study, we examined the association between Grays impulsivity as defined by the RST and event-related fMRI BOLD-response to anticipation of reward in twenty healthy human subjects in brain regions previously associated with reward processing. Anticipation of reward during a Monetary Incentive Delay Task elicited activation in key components of the human reward circuitry such as the ventral striatum, the amygdala and the orbitofrontal cortex. Interindividual differences in Grays impulsivity accounted for a significant amount of variance of the reward-related BOLD-response in the ventral striatum and the orbitofrontal cortex. Specifically, higher trait reward sensitivity was associated with increased activation in response to cues indicating potential reward. Extending previous evidence, here we show that variance in functional brain activation during anticipation of reward is attributed to interindividual differences regarding Grays dimension of impulsivity. Thus, trait reward sensitivity contributes to the modulation of responsiveness in major components of the human reward system which thereby display a core property of the BAS. Generally, fostering our understanding of the neural underpinnings of the association of reward-related interindividual differences in affective traits might aid researchers in quest for custom-tailored treatments of psychiatric disorders, further disentangling the complex relationship between personality traits, emotion, and health.

Simultaneous multislice (SMS) imaging techniques

Simultaneous multislice imaging (SMS) using parallel image reconstruction has rapidly advanced to become a major imaging technique. The primary benefit is an acceleration in data acquisition that is equal to the number of simultaneously excited slices. Unlike in‐plane parallel imaging this can have only a marginal intrinsic signal‐to‐noise ratio penalty, and the full acceleration is attainable at fixed echo time, as is required for many echo planar imaging applications. Furthermore, for some implementations SMS techniques can reduce radiofrequency (RF) power deposition. In this review the current state of the art of SMS imaging is presented. In the Introduction, a historical overview is given of the history of SMS excitation in MRI. The following section on RF pulses gives both the theoretical background and practical application. The section on encoding and reconstruction shows how the collapsed multislice images can be disentangled by means of the transmitter pulse phase, gradient pulses, and most importantly using multichannel receiver coils. The relationship between classic parallel imaging techniques and SMS reconstruction methods is explored. The subsequent section describes the practical implementation, including the acquisition of reference data, and slice cross‐talk. Published applications of SMS imaging are then reviewed, and the article concludes with an outlook and perspective of SMS imaging. Magn Reson Med 75:63–81, 2016.

Parallel magnetic resonance imaging using the GRAPPA operator formalism

In this article it is shown that GRAPPA reconstruction can be reformulated as a matrix operator, similar to ladder or propagator operators used in quantum mechanics, that shifts data in k‐space. Using this formalism, it is shown that there exists an infinitesimal GRAPPA operator that shifts data in k‐space by arbitrarily small amounts. Other desired k‐space shifts can then be accomplished through repeated applications of this infinitesimal GRAPPA operator. Implications of these ideas are described. Magn Reson Med, 2005.

Non-Cartesian Data Reconstruction Using GRAPPA Operator Gridding (GROG)

A novel approach that uses the concepts of parallel imaging to grid data sampled along a non‐Cartesian trajectory using GRAPPA operator gridding (GROG) is described. GROG shifts any acquired data point to its nearest Cartesian location, thereby converting non‐Cartesian to Cartesian data. Unlike other parallel imaging methods, GROG synthesizes the net weight for a shift in any direction from a single basis set of weights along the logical k‐space directions. Given the vastly reduced size of the basis set, GROG calibration and reconstruction requires fewer operations and less calibration data than other parallel imaging methods for gridding. Instead of calculating and applying a density compensation function (DCF), GROG requires only local averaging, as the reconstructed points fall upon the Cartesian grid. Simulations are performed to demonstrate that the root mean square error (RMSE) values of images gridded with GROG are similar to those for images gridded using the gold‐standard convolution gridding. Finally, GROG is compared to the convolution gridding technique using data sampled along radial, spiral, rosette, and BLADE (a.k.a. periodically rotated overlapping parallel lines with enhanced reconstruction [PROPELLER]) trajectories. Magn Reson Med, 2007.

2D‐GRAPPA‐operator for faster 3D parallel MRI

When using parallel MRI (pMRI) methods in combination with three‐dimensional (3D) imaging, it is beneficial to subsample the k‐space along both phase‐encoding directions because one can then take advantage of coil sensitivity variations along two spatial dimensions. This results in an improved reconstruction quality and therefore allows greater scan time reductions as compared to subsampling along one dimension. In this work we present a new approach based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique that allows Fourier‐domain reconstructions of data sets that are subsampled along two dimensions. The method works by splitting the 2D reconstruction process into two separate 1D reconstructions. This approach is compared with an extension of the conventional GRAPPA method that directly regenerates missing data points of a 2D subsampled k‐space by performing a linear combination of acquired data points. In this paper we describe the theoretical background and present computer simulations and in vivo experiments. Magn Reson Med, 2006.