2D partially parallel imaging with k-space surrounding neighbors-based data reconstruction.

Partially parallel imaging (PPI) achieves imaging acceleration by replacing partial phase encoding (PE) with the spatially localized sensitivity encoding of a receiver surface coil array. Further accelerations can be achieved through 2D PPI along two PE directions in 3D MRI. This paper is to explore the k-space-based PPI acquisition and reconstruction strategies for 3D MRI. A surrounding neighbors-based autocalibrating PPI (SNAPPI) was first presented by generalizing the 2D multicolumn multiline interpolation method. Several 2D PPI reconstruction methods were then provided by applying SNAPPI to recover the partially skipped k-space data along two PE directions separately or nonseparately, in k-space or in the hybrid k and image space. An optimal 2D PPI sampling-based reconstruction approach was also presented for applying PPI along certain spatial direction along which the array coil has not sufficient sensitivity variation for a valid PPI reconstruction. Both simulated and in vivo 2D PPI data were used to evaluate the proposed methods.     

Partially parallel imaging with phase-sensitive data: Increased temporal resolution for magnetic resonance temperature imaging.

Magnetic resonance temperature imaging can be used to monitor the progress of thermal ablation therapies, increasing treatment efficacy and improving patient safety. High temporal resolution is important when therapies rapidly heat tissue, but many approaches to faster image acquisition compromise image resolution, slice coverage, or phase sensitivity. Partially parallel imaging techniques offer the potential for improved temporal resolution without forcing such concessions. Although these techniques perturb image phase, relative phase changes between dynamically acquired phase-sensitive images, such as those acquired for MR temperature imaging, can be reliably measured through partially parallel imaging techniques using reconstruction filters that remain constant across the series. Partially parallel and non-accelerated phase-difference-sensitive data can be obtained through arrays of surface coils using this method. Average phase differences measured through partially parallel and fully Fourier encoded images are virtually identical, while phase noise increases with g(sqrt)L as in standard partially parallel image acquisitions..     

Bayesian parallel imaging with edge-preserving priors.

Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a nonconvex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.     

K-t sparse GROWL: sequential combination of partially parallel imaging and compressed sensing in k-t space using flexible virtual coil

Because dynamic MR images are often sparse in x-f domain, k-t space compressed sensing (k-t CS) has been proposed for highly accelerated dynamic MRI. When a multichannel coil is used for acquisition, the combination of partially parallel imaging and k-t CS can improve the accuracy of reconstruction. In this work, an efficient combination method is presented, which is called k-t sparse Generalized GRAPPA fOr Wider readout Line. One fundamental aspect of this work is to apply partially parallel imaging and k-t CS sequentially. A partially parallel imaging technique using a Generalized GRAPPA fOr Wider readout Line operator is adopted before k-t CS reconstruction to decrease the reduction factor in a computationally efficient way while preserving temporal resolution. Channel combination and relative sensitivity maps are used in the flexible virtual coil scheme to alleviate the k-t CS computational load with increasing number of channels. Using k-t FOCUSS as a specific example of k-t CS, the experiments with Cartesian and radial data sets demonstrate that k-t sparse Generalized GRAPPA fOr Wider readout Line can produce results with two times lower root-mean-square error than conventional channel-by-channel k-t CS while consuming up to seven times less computational cost.     

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. Magn Reson Med 69:1109–1114, 2013. © 2012 Wiley Periodicals, Inc.     

Simulation-based investigation of partially parallel imaging with a linear array at high accelerations.

Partially parallel imaging strategies such as SMASH, SENSE, and PILS rely on the sensitivity distribution of phased array RF coils to reduce MRI imaging time. Using an N-element phased array, these techniques allow maximum accelerations, L, such that L < or = N, with acceleration defined as the factor by which scan time is reduced in comparison to traditional, fully gradient encoded acquisitions. As N increases in modern MRI facilities or using special hardware extensions, its role as the primary limitation in partially parallel imaging will be reduced and other limiting factors will become dominant. Two such factors include available SNR and the variation of sensitivity distributions with imaging depth. Simulations have been conducted to evaluate the impact of slice depth and noise on partially parallel reconstructions for the case of a square linear array of overlapped elements that are parallel to the imaging plane. Results indicate that even when sensitivity distributions are exactly known, the linear surface array can only provide high accelerations over a limited imaging depth due to changing suitability of the sensitivity distributions for partially parallel reconstruction. This work emphasizes the importance of simulations for target-based partially parallel array design.     

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.     

Autocalibrating parallel imaging of in vivo trabecular bone microarchitecture at 3 Tesla.

In this work the generalized autocalibrating partially parallel acquisition (GRAPPA) technique was implemented with modified reconstruction and applied to in vivo high-resolution (HR) magnetic resonance imaging (MRI) of the trabecular bone microarchitecture at 3 Tesla (T) with a multiple-acquisition balanced steady-state free precession (b-SSFP) sequence. Trabecular bone is made up of a network of microstructures (80-140 microm), and its structural deterioration is associated with the skeletal metabolic disorder osteoporosis. HR-MRI is a promising noninvasive tool for assessing the trabecular microarchitecture in vivo, but it involves long acquisition times. Using partially parallel imaging (PPI) to accelerate the acquisition may help mitigate this shortcoming and allow more flexibility in protocol design. In this study the effects of GRAPPA-based reconstruction on image characteristics and the measurement of trabecular bone structural parameters were evaluated. Initial studies showed that image quality and depiction of microstructure were preserved in the GRAPPA-based reconstruction, indicating the feasibility of PPI in HR-MRI of trabecular bone. The results also demonstrated the potential of PPI for increasing the signal-to-noise ratio (SNR) efficiency of multiple-acquisition b-SSFP imaging protocols.     

Virtual coil concept for improved parallel MRI employing conjugate symmetric signals

A new approach for utilizing conjugate k‐space symmetry for improved parallel MRI performance is presented. By generating virtual coils containing conjugate symmetric k‐space signals from actual coils, additional image‐ and coil‐phase information can be incorporated into the reconstruction process for parallel acquisition techniques. In that way the reconstruction conditions are improved, resulting in less noise enhancement. In particular in combination with generalized autocalibrating partially parallel acquisitions (GRAPPA), the virtual coil concept represents a practical approach since no explicit spatial phase information is required. In addition, the influence of phase variations originating from the complex receiver coils as well as from the background is investigated. It is shown that there exist background phase distributions yielding an optimized pMRI reconstruction. Magn Reson Med 61:93–102, 2009. © 2008 Wiley‐Liss, Inc.     

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.     

Using UNFOLD to remove artifacts in parallel imaging and in partial-Fourier imaging.

In dynamic MRI, it is often difficult to achieve the acquisition speed required to resolve or freeze the temporal variations of the imaged object. Several MRI methods aim at speeding up the image acquisition process. Through assumptions and/or prior knowledge, these dynamic MRI methods allow part of the needed data to be calculated instead of acquired. For example, partial-Fourier imaging assumes that phase varies smoothly within the object, and parallel imaging (e.g., simultaneous acquisition of spatial harmonics (SMASH) and sensitivity encoding (SENSE)) uses prior knowledge about receiver-coil sensitivity. While these methods accelerate acquisition, they can introduce artifacts or amplify noise in doing so. The present work aims at accelerating image acquisition significantly, while introducing almost no artifacts or noise amplification. It is shown here that new, extra information is gained if dynamic MRI methods are modified so that the sampling function changes in specific ways from time-frame to time-frame. In other words, the set of k-space locations that are acquired (instead of calculated) changes with time. The present temporal strategy, based on the UNaliasing by Fourier-encoding the Overlaps in the temporaL Dimension (UNFOLD) method, can be incorporated into common dynamic MRI methods. Results with partial-Fourier, SMASH, and SENSE imaging are presented here, where UNFOLD's contribution is to very significantly reduce the artifact and/or amplified noise content. Used in this way, UNFOLD contributes indirectly, rather than directly to the improvement in image acquisition speed, as it allows companion methods to operate properly at greater acceleration settings than would otherwise be feasible.     

Parallel and partial Fourier imaging with prospective motion correction

Subject motion during scan is a major source of artifacts in MR examinations. Prospective motion correction is a promising technique that tracks subject motion and adjusts the imaging volume in real time; however, additional retrospective correction may be necessary to achieve robust image quality and compatibility with other imaging options. Real‐time realignment of the imaging volume by prospective motion correction changes the coil sensitivity weighting and the field inhomogeneity relative to the imaging volume. This can pose image reconstruction problems with parallel imaging and partial Fourier imaging, which rely on coil sensitivity and image phase information, respectively. This work presents a practical method for reconstructing images acquired using prospective motion correction with parallel imaging and/or partial Fourier imaging. Our proposed approach is data driven and noniterative; data are binned into several position bins based on motion measurements made during the prospective motion correction acquisition and the data in each bin are processed through intrabin operations such as parallel imaging reconstruction (in case of undersampling), phase correction, and coil combination before combination of the position bins. We demonstrate the effectiveness of our technique through simulation studies and in vivo experiments using a prospectively motion‐corrected three‐dimensional fast spin echo sequence. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Inherently self-calibrating non-Cartesian parallel imaging.

The use of self-calibrating techniques in parallel magnetic resonance imaging eliminates the need for coil sensitivity calibration scans and avoids potential mismatches between calibration scans and subsequent accelerated acquisitions (e.g., as a result of patient motion). Most examples of self-calibrating Cartesian parallel imaging techniques have required the use of modified k-space trajectories that are densely sampled at the center and more sparsely sampled in the periphery. However, spiral and radial trajectories offer inherent self-calibrating characteristics because of their densely sampled center. At no additional cost in acquisition time and with no modification in scanning protocols, in vivo coil sensitivity maps may be extracted from the densely sampled central region of k-space. This work demonstrates the feasibility of self-calibrated spiral and radial parallel imaging using a previously described iterative non-Cartesian sensitivity encoding algorithm.