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.

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Simultaneous multislice (SMS) imaging techniques

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 62: 739 –746, 2009. © 2009 Wiley-Liss, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.22066

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

The class of autocalibrating "data-driven" parallel imaging (PI) methods has gained attention in recent years due to its ability to achieve high quality reconstructions even under challenging imaging conditions. The aim of this work was to perform a formal comparative study of various data-driven reconstruction techniques to evaluate their relative merits for certain imaging applications. A total of five different reconstruction methods are presented within a consistent theoretical framework and experimentally compared in terms of the specific measures of reconstruction accuracy and efficiency using one-dimensional (1D)-accelerated Cartesian datasets. It is shown that by treating the reconstruction process as two discrete phases, a calibration phase and a synthesis phase, the reconstruction pathway can be tailored to exploit the computational advantages available in certain data domains. A new "split-domain" reconstruction method is presented that performs the calibration phase in k-space (k(x), k(y)) and the synthesis phase in a hybrid (x, k(y)) space, enabling highly accurate 2D neighborhood reconstructions to be performed more efficiently than previously possible with conventional techniques. This analysis may help guide the selection of PI methods for a given imaging task to achieve high reconstruction accuracy at minimal computational expense.

/pdf/comparison-of-reconstruction-accuracy-and-efficiency-among-39vgxsz38c.pdf

Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods.

3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction.

The present invention provides a system and method for parallel imaging that performs auto-calibrating reconstructions with a 2D (for 2D imaging) or 3D kernel (for 3D imaging) that exploits the computational efficiencies available when operating in certain data “domains” or “spaces”. The reconstruction process of multi-coil data is separated into a “training phase” and an “application phase” in which reconstruction weights are applied to acquired data to synthesize (replace) missing data. The choice of data space, i.e., k-space, hybrid space, or image space, in which each step occurs is independently optimized to reduce total reconstruction time for a given imaging application. As such, the invention retains the image quality benefits of using a 2D k-space kernel without the computational burden of applying a 2D k-space convolution kernel.

Method and apparatus of multi-coil MR imaging with hybrid space calibration

full kCh image represent the fitted image of the channel kCh , ppa iCh image image data of undersampled raw data of the channel iCh and kCh iCh f , the fitting coefficient, which is obtained by the Fourier transform of the convolution

Y. Zhuo

Papers

Image Domain Based Fast GRAPPA Reconstruction and relative SNR degradation Factor