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 under-sampled 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.     

Field-of-view limitations in parallel imaging.

Parallel imaging is one of the most promising developments in recent years for the acceleration of MR acquisitions. One area of practical importance where different parallel imaging methods perform differently is the manner in which they deal with aliasing in the full-FOV reconstructed image. It has been reported that sensitivity encoding (SENSE) reconstruction fails whenever the reconstructed FOV is smaller than the object being imaged. On the other hand, generalized autocalibrating partially parallel acquisition (GRAPPA) has been used successfully to reconstruct images with aliasing in the reconstructed FOV, as in conventional imaging. The disparate behavior of these methods can be easily demonstrated by a few simple illustrative examples. Additional in vivo examples using GRAPPA and modified SENSE (mSENSE) make this distinction clear. These experiments demonstrate that SENSE fails to reconstruct correct images when coil sensitivity maps are used that do not automatically account for the object size and therefore the aliasing in the reconstructed images. However, with the use of aliased high-resolution coil sensitivity maps, accurate SENSE reconstructions can be generated. On the other hand, GRAPPA produces images with an aliasing appearance that is exactly as would be expected from normal nonaccelerated acquisitions. An understanding of these effects could potentially lead to fewer operator-dependent errors, as well as a better understanding of the differences between the underlying reconstruction processes.     

Improvements in multislice parallel imaging using radial CAIPIRINHA

Multislice parallel imaging involves the simultaneous sampling of multiple parallel slices which are subsequently separated using parallel imaging reconstruction. The CAIPIRINHA technique improves this reconstruction by manipulating the phase of the RF excitation pulses to shift the aliasing pattern such that there is less aliasing energy to be reconstructed. In this work, it is shown that combining the phase manipulation used in CAIPIRINHA with a non‐Cartesian (radial) sampling scheme further decreases the aliasing energy for the parallel imaging algorithm to reconstruct, thereby further increasing the degree to which a multi‐channel receiver array can be utilized for parallel imaging acceleration. In radial CAIPIRINHA, individual bands (slices) in a multislice excitation are modulated with view‐dependent phase, causing a destructive interference of entire slices. This destructive interference leads to a reduction in aliasing compared to the coherent shifts one observes when using this same technique with a Cartesian trajectory. Recovery of each individual slice is possible because the applied phase pattern is known, and a conjugate‐gradient reconstruction algorithm minimizes the contributions from other slices. Results are presented with a standard 12‐channel head coil with acceleration factors up to 14, where radial CAIPIRINHA produces an improved reconstruction when compared with Cartesian CAIPIRINHA. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

Fast method for 1D non-cartesian parallel imaging using GRAPPA.

MRI with non-Cartesian sampling schemes can offer inherent advantages. Radial acquisitions are known to be very robust, even in the case of vast undersampling. This is also true for 1D non-Cartesian MRI, in which the center of k-space is oversampled or at least sampled at the Nyquist rate. There are two main reasons for the more relaxed foldover artifact behavior: First, due to the oversampling of the center, high-energy foldover artifacts originating from the center of k-space are avoided. Second, due to the non-equidistant sampling of k-space, the corresponding field of view (FOV) is no longer well defined. As a result, foldover artifacts are blurred over a broad range and appear less severe. The more relaxed foldover artifact behavior and the densely sampled central k-space make trajectories of this type an ideal complement to autocalibrated parallel MRI (pMRI) techniques, such as generalized autocalibrating partially parallel acquisitions (GRAPPA). Although pMRI can benefit from non-Cartesian trajectories, this combination has not yet entered routine clinical use. One of the main reasons for this is the need for long reconstruction times due to the complex calculations necessary for non-Cartesian pMRI. In this work it is shown that one can significantly reduce the complexity of the calculations by exploiting a few specific properties of k-space-based pMRI.     

Direct parallel image reconstructions for spiral trajectories using GRAPPA.

The use of spiral trajectories is an efficient way to cover a desired k-space partition in magnetic resonance imaging (MRI). Compared to conventional Cartesian k-space sampling, it allows faster acquisitions and results in a slight reduction of the high gradient demand in fast dynamic scans, such as in functional MRI (fMRI). However, spiral images are more susceptible to off-resonance effects that cause blurring artifacts and distortions of the point-spread function (PSF), and thereby degrade the image quality. Since off-resonance effects scale with the readout duration, the respective artifacts can be reduced by shortening the readout trajectory. Multishot experiments represent one approach to reduce these artifacts in spiral imaging, but result in longer scan times and potentially increased flow and motion artifacts. Parallel imaging methods are another promising approach to improve image quality through an increase in the acquisition speed. However, non-Cartesian parallel image reconstructions are known to be computationally time-consuming, which is prohibitive for clinical applications. In this study a new and fast approach for parallel image reconstructions for spiral imaging based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) methodology is presented. With this approach the computational burden is reduced such that it becomes comparable to that needed in accelerated Cartesian procedures. The respective spiral images with two- to eightfold acceleration clearly benefit from the advantages of parallel imaging, such as enabling parallel MRI single-shot spiral imaging with the off-resonance behavior of multishot acquisitions.     

Non‐Cartesian parallel imaging reconstruction

Non‐Cartesian parallel imaging has played an important role in reducing data acquisition time in MRI. The use of non‐Cartesian trajectories can enable more efficient coverage of k‐space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be used to reconstruct images from undersampled Cartesian data, non‐Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the nonhomogeneous sensitivities of multi‐coil phased arrays. This review will begin with an overview of non‐Cartesian k‐space trajectories and their sampling properties, followed by an in‐depth discussion of several selected non‐Cartesian parallel imaging algorithms. Three representative non‐Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non‐Cartesian generalized autocalibrating partially parallel acquisition (GRAPPA), and Iterative Self‐Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non‐Cartesian parallel imaging will be covered. J. Magn. Reson. Imaging 2014;40:1022–1040 . © 2014 Wiley Periodicals, Inc.     

Breathheld autocalibrated phase‐contrast imaging

Purpose:

To compare generalized autocalibrating partially parallel acquisitions (GRAPPA), modified sensitivity encoding (mSENSE), and SENSE in phase‐contrast magnetic resonance imaging (PC‐MRI) applications.

Materials and Methods:

Aliasing of the torso can occur in PC‐MRI applications. If the data are further undersampled for parallel imaging, SENSE can be problematic in correctly unaliasing signals due to coil sensitivity maps that do not match that of the aliased volume. Here, a method for estimating coil sensitivities in flow applications is described. Normal volunteers (n = 5) were scanned on a 1.5 T MRI scanner and underwent PC‐MRI scans using GRAPPA, mSENSE, SENSE, and conventional PC‐MRI acquisitions. Peak velocity and flow through the aorta and pulmonary artery were evaluated.

Results:

Bland–Altman statistics for flow in the aorta and pulmonary artery acquired with mSENSE and GRAPPA methods (R = 2 and R = 3 cases) have comparable mean differences to flow acquired with conventional PC‐MRI. GRAPPA and mSENSE PC‐MRI have more robust measurements than SENSE when there is aliasing artifact caused by insufficient coil sensitivity maps. For peak velocity, there are no considerable differences among the mSENSE, GRAPPA, and SENSE reconstructions and are comparable to conventional PC‐MRI.

Conclusion:

Flow measurements of images reconstructed with autocalibration techniques have comparable agreement with conventional PC‐MRI and provide robust measurements in the presence of wraparound. J. Magn. Reson. Imaging 2010;31:1004–1014. ©2010 Wiley‐Liss, Inc.     

Using GRAPPA to improve autocalibrated coil sensitivity estimation for the SENSE family of parallel imaging reconstruction algorithms

Two strategies are widely used in parallel MRI to reconstruct subsampled multicoil image data. SENSE and related methods employ explicit receiver coil spatial response estimates to reconstruct an image. In contrast, coil‐by‐coil methods such as GRAPPA leverage correlations among the acquired multicoil data to reconstruct missing k‐space lines. In self‐referenced scenarios, both methods employ Nyquist‐rate low‐frequency k‐space data to identify the reconstruction parameters. Because GRAPPA does not require explicit coil sensitivities estimates, it needs considerably fewer autocalibration signals than SENSE. However, SENSE methods allow greater opportunity to control reconstruction quality though regularization and thus may outperform GRAPPA in some imaging scenarios. Here, we employ GRAPPA to improve self‐referenced coil sensitivity estimation in SENSE and related methods using very few auto‐calibration signals. This enables one to leverage each methods' inherent strength and produce high quality self‐referenced SENSE reconstructions. Magn Reson Med 60:462–467, 2008. © 2008 Wiley‐Liss, Inc.     

Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction.

In this work an iterative reconstruction method based on generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is introduced. In the new method the reconstructed lines are used to reestimate and refine the weights from all the acquired data by applying the GRAPPA procedure iteratively with regularization. Both phantom and in vivo MRI experiments demonstrated that, compared to GRAPPA, the iterative approach reduces parallel imaging artifacts and permits high-quality image reconstruction with a relatively small number of calibration lines and slight changes of GRAPPA weights.     

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.     

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.     

Auto-calibrated parallel spiral imaging.

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is defined along radial directions so that missing spiral interleaves can be estimated directly from neighboring interleaves. This kernel is invariant along the radial direction but varies azimuthally. Therefore, the k-space is divided into angular sectors and sector-specific kernels are used. It is demonstrated experimentally that relatively few sectors are sufficient for accurate reconstruction, allowing for efficient implementation. The interpolation kernels can be derived either from a separate calibration scan or self-calibration data available with a dual-density spiral acquisition. The reconstruction method is implemented with two sampling strategies and experimentally demonstrated to be robust.     

Parallel image reconstruction using B-spline approximation (PROBER).

A new reconstruction method for parallel MRI called PROBER is proposed. The method PROBER works in an image domain similar to methods based on Sensitivity Encoding (SENSE). However, unlike SENSE, which first estimates the spatial sensitivity maps, PROBER approximates the reconstruction coefficients directly by B-splines. Also, B-spline coefficients are estimated at once in order to minimize the reconstruction error instead of estimating the reconstruction in each pixel independently (as in SENSE). This makes the method robust to noise in reference images. No presmoothing of reference images is necessary. The number of estimated parameters is reduced, which speeds up the estimation process. PROBER was tested on simulated, phantom, and in vivo data. The results are compared with commercial implementations of the algorithms SENSE and GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) in terms of elapsed time and reconstruction quality. The experiments showed that PROBER is faster than GRAPPA and SENSE for images wider than 150x150 pixels for comparable reconstruction quality. With more basis functions, PROBER outperforms both SENSE and GRAPPA in reconstruction quality at the cost of slightly increased computational time.     

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.     

High-pass GRAPPA: an image support reduction technique for improved partially parallel imaging.

Partially parallel imaging (PPI) is a widely used technique in clinical applications. A limitation of this technique is the strong noise and artifact in the reconstructed images when high reduction factors are used. This work aims to increase the clinical applicability of PPI by improving its performance at high reduction factors. A new concept, image support reduction, is introduced. A systematic filter-design approach for image support reduction is proposed. This approach shows more advantages when used with an important existing PPI technique, GRAPPA. An improved GRAPPA method, high-pass GRAPPA (hp-GRAPPA), was developed based on this approach. The new technique does not involve changing the original GRAPPA kernel and performs reconstruction in almost the same amount of time. Experimentally, it is demonstrated that the reconstructed images using hp-GRAPPA have much lower noise/artifact level than those reconstructed using GRAPPA.     

Continuous ASL (CASL) perfusion MRI with an array coil and parallel imaging at 3T.

The purpose of this work was to assess the feasibility and efficacy of using an array coil and parallel imaging in continuous arterial spin labeling (CASL) perfusion MRI. An 8-channel receive-only array head coil was used in conjunction with a surrounding detunable volume transmit coil. The signal to noise ratio (SNR), temporal stability, cerebral blood flow (CBF), and perfusion image coverage were measured from steady state CASL scans using: a standard volume coil, array coil, and array coil with 2- and 3-fold accelerated parallel imaging. Compared to the standard volume coil, the array coil provided 3 times the average SNR increase and higher temporal stability for the perfusion weighted images, even with threefold acceleration. Although perfusion images of the array coil were affected by the inhomogeneous coil sensitivities, this effect was invisible in the quantitative CBF images, which showed highly reproducible perfusion values compared to the standard volume coil. The unfolding distortions of parallel imaging were suppressed in the perfusion images by pairwise subtraction, though they sharply degraded the raw EPI images. Moreover, parallel imaging provided the potential of acquiring more slices due to the shortened acquisition time and improved coverage in brain regions with high static field inhomogeneity. Such results highlight the potential utility of array coils and parallel imaging in ASL perfusion MRI.     

Zigzag sampling for improved parallel imaging

Conventional Cartesian parallel MRI methods are limited to the sensitivity variations provided by the underlying receiver coil array in the dimension in which the data reduction is carried out, namely, the phase‐encoding directions. However, in this work an acquisition strategy is presented that takes advantage of sensitivity variations in the readout direction, thus improving the parallel imaging reconstruction process. This is achieved by employing rapidly oscillating phase‐encoding gradients during the actual readout. The benefit of this approach is demonstrated in vivo using various zigzag‐shaped gradient trajectory designs. It is shown that zigzag type sampling, in analogy to CAIPIRINHA, modifies the appearance of aliasing in 2D and 3D imaging, thereby utilizing additional sensitivity variations in the readout direction directly resulting in improved parallel imaging reconstruction performance. Magn Reson Med 60:474–478, 2008. © 2008 Wiley‐Liss, Inc.