Generalized Autocalibrating Partially Parallel Acquisitions Research Articles

Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.

Impact of reduced k‐space acquisition on pathologic detectability for volumetric MR spectroscopic imaging

PurposeTo assess the impact of accelerated acquisitions on the spectral quality of volumetric magnetic resonance spectroscopic imaging (MRSI) and to evaluate their ability in detecting metabolic changes with mild injury.Materials and MethodsThe implementation of a generalized autocalibrating partially parallel acquisition (GRAPPA) method for a high‐resolution whole‐brain echo planar SI (3D‐EPSI) sequence is first described and the spectral accuracy of the GRAPPA‐EPSI method is investigated using lobar and voxel‐based analyses for normal subjects and patients with mild traumatic brain injuries (mTBI). The performance of GRAPPA was compared with that of fully encoded EPSI for five datasets collected from normal subjects at the same scanning session, as well as on 45 scans (20 normal subjects and 25 mTBI patients) for which the reduced k‐space sampling was simulated. For comparison, a central k‐space lower‐resolution 3D‐EPSI acquisition was also simulated. Differences in individual metabolites and metabolite ratio distributions of the mTBI group relative to those of age‐matched control subjects were statistically evaluated using analyses divided into hemispheric brain lobes and tissue types.ResultsGRAPPA‐EPSI with 16‐minute scan time yielded robust and similar results in terms of MRSI quantitation, spectral fitting, and accuracy with that of fully sampled 3D‐EPSI acquisitions and was more accurate than central k‐space acquisition. Primary findings included high correlations (accuracy of 92.6%) between the GRAPPA and fully sampled results.ConclusionAlthough the reduced encoding method is associated with lower signal‐to‐noise ratio (SNR) that impacts the quality of spectral analysis, the use of the parallel imaging method can lead to the same diagnostic outcomes as the fully sampled data when using the sensitivity‐limited volumetric MRSI. J. Magn. Reson. Imaging 2014;39:224–234. © 2013 Wiley Periodicals, Inc.

K‐t GRAPPA accelerated phase contrast MRI: Improved assessment of blood flow and 3‐directional myocardial motion during breath‐hold

To evaluate spatiotemporal parallel imaging with R = 5 in comparison to conventional parallel imaging with R = 2 applied to phase contrast (PC) magnetic resonance imaging (MRI). This was motivated by the fact that scan times for PC imaging often exceed breath-hold capabilities of patients even with standard parallel imaging using typical reduction factors of R = 2. K-t generalized autocalibrating partially parallel acquisition (GRAPPA) acquisition was validated in phantom measurements and then applied in 10 volunteer and three patient examinations. Due to the higher reduction factor compared to conventional GRAPPA, k-t GRAPPA measurements could be performed during breath-hold with high spatial and temporal resolution. K-t GRAPPA scans were compared to GRAPPA acquired during free-breathing with navigator respiration control. In addition, spatiotemporally accelerated PC imaging was acquired during free-breathing for comparison of k-t-accelerated breath-held scans. Substantial improvements in image quality for the breath-hold measurements were observed. Significantly reduced peak velocities were found for the GRAPPA protocol compared to the k-t-accelerated breath-hold scans for both flow (8%) and myocardial motion (up to 30%) measurements. Spatiotemporal acceleration allows the performance of high temporal or spatial resolution PC imaging during breath-hold while providing high image quality and robust acquisition of functional information that cannot be achieved during breath-hold with standard techniques.

Diffusion‐weighted 3D multislab echo planar imaging for high signal‐to‐noise ratio efficiency and isotropic image resolution

To acquire isotropic high-resolution, signal-to-noise ratio (SNR) efficient, 3D encoded diffusion-weighted MRI data. Multiple Fourier encoded slabs were combined into one full volume, using a generalized autocalibrating partially parallel acquisitions (GRAPPA) accelerated diffusion-weighted 3D multislab echo planar imaging (EPI) sequence with 2D phase navigation and in-plane motion correction. Reconstructed data with 1.5-mm(3) nominal resolution is presented and shown under the influence of motion and with variable slab thicknesses. The SNR efficiency between diffusion-weighted 3D multislab EPI and DW 2D ss-EPI is compared. Finally, a 1.3-mm(3) full brain scan with 45 diffusion directions is presented. Diffusion-weighted 3D multislab EPI has been presented as an alternative sequence for high-resolution and high-SNR full brain coverage diffusion studies. Compared with the gold standard 2D diffusion-weighted single-shot Echo Planar Imaging, the SNR efficiency is significantly higher.

Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense)

AbstractSENSitivity Encoding (SENSE) is a parallel MR image reconstruction technique that yields optimal results when the sensitivity maps are accurately known. Unfortunately, in practical scenarios, obtaining accurate estimates of the sensitivity maps is not possible. In this work, we propose a technique that iteratively reconstructs the image and refines the sensitivity maps (from initial estimates). Our technique is named iSENSE (iterative SENSE). Our proposed technique exploits the sparsity of the MR image in some transform domains or the rank deficiency characteristic of the matrix representing the MRI image; the former leads to a compressed sensing‐based reconstruction method, whereas the latter leads to an image reconstruction method that minimizes the nuclear norm (NN) of the image matrix. The sensitivity maps are assumed to be rank‐deficient matrices, and thus the refinement of the sensitivity maps is achieved via the NN minimization. To evaluate the performance of the proposed method, we have carried out the experiments on real and one simulated datasets. We have compared our method with three state‐of‐the‐art image domain methods—SparSENSE (Sparse SENSE), NNSENSE (NN Regularized SENSE), and JSENSE (Joint SENSE reconstruction)—and one widely used frequency domain method—GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition). Our method yields the best reconstruction results both in quantitative (normalized mean‐squared error) and qualitative (visual inspection of reconstructed and difference images) evaluation. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 269–280, 2012.

A simple application of compressed sensing to further accelerate partially parallel imaging

Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, “CS+GRAPPA,” to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.

Diffusion tensor imaging (DTI) with retrospective motion correction for large‐scale pediatric imaging

To develop and implement a clinical DTI technique suitable for the pediatric setting that retrospectively corrects for large motion without the need for rescanning and/or reacquisition strategies, and to deliver high-quality DTI images (both in the presence and absence of large motion) using procedures that reduce image noise and artifacts. We implemented an in-house built generalized autocalibrating partially parallel acquisitions (GRAPPA)-accelerated diffusion tensor (DT) echo-planar imaging (EPI) sequence at 1.5T and 3T on 1600 patients between 1 month and 18 years old. To reconstruct the data, we developed a fully automated tailored reconstruction software that selects the best GRAPPA and ghost calibration weights; does 3D rigid-body realignment with importance weighting; and employs phase correction and complex averaging to lower Rician noise and reduce phase artifacts. For select cases we investigated the use of an additional volume rejection criterion and b-matrix correction for large motion. The DTI image reconstruction procedures developed here were extremely robust in correcting for motion, failing on only three subjects, while providing the radiologists high-quality data for routine evaluation. This work suggests that, apart from the rare instance of continuous motion throughout the scan, high-quality DTI brain data can be acquired using our proposed integrated sequence and reconstruction that uses a retrospective approach to motion correction. In addition, we demonstrate a substantial improvement in overall image quality by combining phase correction with complex averaging, which reduces the Rician noise that biases noisy data.

Improved parallel MR imaging using a coefficient penalized regularization for GRAPPA reconstruction

A novel coefficient penalized regularization method for generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is developed for improving MR image quality. In this method, the fitting coefficients of the source data are weighted with different penalty factors, which are highly dependent upon the relative displacements from the source data to the target data in k-space. The imaging data from both phantom testing and in vivo MRI experiments demonstrate that the coefficient penalized regularization method in GRAPPA reconstruction is able to reduce noise amplification to a greater degree. Therefore, the method enhances the quality of images significantly when compared to the previous least squares and Tikhonov regularization methods.

Flexible transceiver array for ultrahigh field human MR imaging

A flexible transceiver array, capable of multiple-purpose imaging applications in vivo at ultrahigh magnetic fields was designed, implemented and tested on a 7 T MR scanner. By alternately placing coil elements with primary and secondary harmonics, improved decoupling among coil elements was accomplished without requiring decoupling circuitry between resonant elements, which is commonly required in high-frequency transceiver arrays to achieve sufficient element-isolation during radiofrequency excitation. This flexible array design is capable of maintaining the required decoupling among resonant elements in different array size and geometry and is scalable in coil size and number of resonant elements (i.e., number of channels), yielding improved filling factors for various body parts with different geometry and size. To investigate design feasibility, flexibility, and array performance, a multichannel, 16-element transceiver array was designed and constructed, and in vivo images of the human head, knee, and hand were acquired using a whole-body 7 T MR system. Seven Tesla parallel imaging with generalized autocalibrating partially parallel acquisitions (GRAPPA) performed using this flexible transceiver array was also presented.

Denoising sparse images from GRAPPA using the nullspace method

To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with generalized autocalibrating partially parallel acquisitions (GRAPPA) alone, the DEnoising of Sparse Images from GRAPPA using the Nullspace method is developed. The trade-off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k-space data using DEnoising of Sparse Images from GRAPPA using the Nullspace method are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), peak-signal-to-noise ratio, and noise amplification (g-factors) as measured using the pseudo-multiple replica method. Effects of smoothing, including contrast loss, are studied in synthetic phantom data. In the experiments presented, the contrast loss and spatial resolution are competitive with existing methods. Results for several brain images demonstrate significant improvements over GRAPPA at high acceleration factors in denoising performance with limited blurring or smoothing artifacts. In addition, the measured g-factors suggest that DEnoising of Sparse Images from GRAPPA using the Nullspace method mitigates noise amplification better than both GRAPPA and L1 iterative self-consistent parallel imaging reconstruction (the latter limited here by uniform undersampling).

Variation of noise in multi-run functional MRI using generalized autocalibrating partially parallel acquisition (GRAPPA)

To investigate the noise variation in multi-run functional MRI (fMRI) scans using generalized autocalibrating partially parallel acquisition (GRAPPA), with a focus on the cause of this variation. A phantom was continuously scanned for 10 runs using echo-planar imaging (EPI) combined with GRAPPA to simulate a multi-run fMRI exam. The variation of noise between runs was examined for different GRAPPA acceleration factors. The noise variation was also evaluated in a real fMRI experiment with human subjects at an acceleration factor of two. The cause of noise variation was explored by offline reconstruction using different GRAPPA weights and numerical simulation of GRAPPA reference scans. It was found that the noise distribution in the image is stable within a run but may vary randomly from run to run. The variation of noise was also observed in fMRI experiments with human subjects. The variation can be significantly reduced if all the images from individual runs are reconstructed using the same reference scan data. Both phantom experiments and human data showed that the noise pattern may change in different fMRI runs. The variation is mainly caused by the random noise in separate reference scans for GRAPPA in each run.

Exploiting sparsity to accelerate noncontrast MR angiography in the context of parallel imaging

Noncontrast techniques for peripheral MR angiography are receiving renewed interest because of safety concerns about the use of gadolinium in patients with renal insufficiency. One class of techniques involves subtraction of dark-blood images acquired during fast systolic flow from bright-blood images obtained during slow diastolic flow. The goal of this work was to determine whether the inherent sparsity of the difference images could be exploited to achieve greater acceleration without loss of image quality in the context of generalized autocalibrating partially parallel acquisition (GRAPPA). It is shown that noise amplification at high acceleration factors can be reduced by performing subtraction on the raw data, before calculation of the GRAPPA weights, rather than on the final magnitude images. Use of the difference data to calculate the GRAPPA weights decreases the geometry factor (g-factor), because the difference data represent a sparse image set. This demonstrates an inherent property of GRAPPA and does not require the use of compressed sensing. Application of this approach to highly accelerated data from healthy volunteers resulted in similar depiction of large arteries to that obtained with low acceleration and standard reconstruction. However, visualization of very small vessels and arterial branches was compromised.

High temporal resolution retrospective motion correction with radial parallel imaging

A method for motion correction in multicoil imaging applications, involving both data collection and reconstruction, is presented. A bit-reversed radial acquisition scheme, in conjunction with a rapid self-calibrated parallel imaging method, Generalized auto-calibrating partial parallel acquisition (GRAPPA) operator for wider radial bands (GROWL), is used to achieve motion correction at a high temporal resolution. View-by-view in-plane motion correction is achieved in 2D imaging, while 3D motion correction is achieved for every two consecutive slice-encoding planes in 3D imaging. In the proposed technique, GROWL contributes in two aspects: First, a central k-space circle/cylinder used as the motion-free reference is generated from a small number of radial lines/planes; Second, undersampled k-space regions resulting from rotation and inconsistent (e.g. intraview and nonrigid body) motion can be filled in. When compared with navigator-based motion correction methods, the proposed method does not prolong scan time and can be applied to short-TR sequences. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc.

Comparison of intravascular and extracellular contrast media for absolute quantification of myocardial rest‐perfusion using high‐resolution MRI

To use the contrast agent gadofosveset for absolute quantification of myocardial perfusion and compare it with gadobenate dimeglumine (Gd-BOPTA) using a high-resolution generalized autocalibrating partially parallel acquisition (GRAPPA) sequence. Ten healthy volunteers were examined twice at two different dates with a first-pass perfusion examination at rest using prebolus technique. We used a 1.5 T scanner and a 32 channel heart-array coil with a steady-state free precession (SSFP) true fast imaging with steady state precession (trueFISP) GRAPPA sequence (acceleration-factor 3). Manual delineation of the myocardial contours was performed and absolute quantification was performed after baseline and contamination correction. At the first appointment, 1cc/4cc of the extracellular contrast agent Gd-BOPTA were administered, on the second date, 1cc/4cc of the blood pool contrast agent (CA) gadofosveset. At each date the examination was repeated after a 15-minute time interval. Using gadofosveset perfusion the value (in cc/g/min) at rest was 0.66 ± 0.25 (mean ± standard deviation) for the first, and 0.55 ± 0.24 for the second CA application; for Gd-BOPTA it was 0.62 ± 0.25 and 0.45 ± 0.23. No significant difference was found between the acquired perfusion values. The apparent mean residence time in the myocardium was 23 seconds for gadofosveset and 19.5 seconds for Gd-BOPTA. Neither signal-to-noise ratio (SNR) nor subjectively rated image contrast showed a significant difference. The application of gadofosveset for an absolute quantification of myocardial perfusion is possible. Yet the acquired perfusion values show no significant differences to those determined with Gd-BOPTA, maintained the same SNR and comparable perfusion values, and did not picture the expected concentration time-course for an intravasal CA in the first pass.

A radial self‐calibrated (RASCAL) generalized autocalibrating partially parallel acquisition (GRAPPA) method using weight interpolation

A generalized autocalibrating partially parallel acquisition (GRAPPA) method for radial k-space sampling is presented that calculates GRAPPA weights without synthesized or acquired calibration data. Instead, GRAPPA weights are fitted to the undersampled data as if they were the calibration data. Because the relative k-space shifts associated with these GRAPPA weights vary for a radial trajectory, new GRAPPA weights can be resampled for arbitrary shifts through interpolation, which are then used to generate missing projections between the acquired projections. The method is demonstrated in phantoms and in abdominal and brain imaging. Image quality is similar to radial GRAPPA using fully sampled calibration data, and improved relative to a previously described self-calibrated radial GRAPPA technique.

A parallel imaging technique using mutual calibration for split‐blade diffusion‐weighted PROPELLER

Split-blade diffusion-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (DW-PROPELLER) was proposed to address the issues associated with diffusion-weighted echo planar imaging such as geometric distortion and difficulty in high-resolution imaging. The major drawbacks with DW-PROPELLER are its high SAR (especially at 3T) and violation of the Carr-Purcell-Meiboom-Gill condition, which leads to a long scan time and narrow blade. Parallel imaging can reduce scan time and increase blade width; however, it is very challenging to apply standard k-space-based techniques such as GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) to split-blade DW-PROPELLER due to its narrow blade. In this work, a new calibration scheme is proposed for k-space-based parallel imaging method without the need of additional calibration data, which results in a wider, more stable blade. The in vivo results show that this technique is very promising.

GRAPPA operator for wider radial bands (GROWL) with optimally regularized self‐calibration

A self-calibrated parallel imaging reconstruction method is proposed for azimuthally undersampled radial dataset. A generalized auto-calibrating partially parallel acquisition (GRAPPA) operator is used to widen each radial view into a band consisting of several parallel lines, followed by a standard regridding procedure. Self-calibration is achieved by regridding the central k-space region, where Nyquist criterion is satisfied, to a rotated Cartesian grid. During the calibration process, an optimal Tikhonov regularization factor is introduced to reduce the error caused by the small k-space area of the self-calibration region. The method was applied to phantom and in vivo datasets acquired with an eight-element coil array, using 32-64 radial views with 256 readout samples. When compared with previous radial parallel imaging techniques, GRAPPA operator for wider radial bands (GROWL) provides a significant speed advantage since calibration is carried out using the fully sampled k-space center. A further advantage of GROWL is its applicability to arbitrary-view angle ordering schemes.

Combination of compressed sensing and parallel imaging for highly accelerated first‐pass cardiac perfusion MRI

First-pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k-t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k-t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k-t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8-fold acceleration in vivo with whole-heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to Generalized autocalibrating partially parallel acquisitions (GRAPPA) with 2-fold acceleration.

New trajectory for image reconstruction based on GRAPPA

The sampling trajectory of traditional GRAPPA(generalized auto-calibrating partially parallel acquisitions) has some problems,such as bad reconstruction image,and long data scanning time.A new trajectory for GRAPPA reconstruction called 3D sampling trajectory was proposed.This method not only undersampled in phase encoding direction,but also in frequency encoding direction and coil direction.The missing data were calculated by a 3D kernel.Brain imaging experiment of real brain data demonstrates that this new trajectory and 3D kernel can make use of coil spatial sensitivity information effectively,improve quality of reconstruction image and reduce time of data scanning.In a word,3D sampling trajectory is an effective method.

Spiral demystified

Spiral acquisition schemes offer unique advantages such as flow compensation, efficient k-space sampling and robustness against motion that make this option a viable choice among other non-Cartesian sampling schemes. For this reason, the main applications of spiral imaging lie in dynamic magnetic resonance imaging such as cardiac imaging and functional brain imaging. However, these advantages are counterbalanced by practical difficulties that render spiral imaging quite challenging. Firstly, the design of gradient waveforms and its hardware requires specific attention. Secondly, the reconstruction of such data is no longer straightforward because k-space samples are no longer aligned on a Cartesian grid. Thirdly, to take advantage of parallel imaging techniques, the common generalized autocalibrating partially parallel acquisitions (GRAPPA) or sensitivity encoding (SENSE) algorithms need to be extended. Finally, and most notably, spiral images are prone to particular artifacts such as blurring due to gradient deviations and off-resonance effects caused by B(0) inhomogeneity and concomitant gradient fields. In this article, various difficulties that spiral imaging brings along, and the solutions, which have been developed and proposed in literature, will be reviewed in detail.