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.     

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.     

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.     

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.     

The effect of reconstruction and acquisition parameters for GRAPPA‐based parallel imaging on the image quality

Parallel imaging based on generalized autocalibrating partially parallel acquisitions is widely used in the clinical routine. To date, no detailed analysis has been presented describing the dependence of the image quality on the reconstruction and acquisition parameters such as the number of autocalibration signal (ACS) lines N_ACS, the reconstruction kernel size (b_x × b_y), and the undersampling factor R. To evaluate their influence on the performance of generalized autocalibrating partially parallel acquisitions, two phantom data sets acquired with 12‐channel and 32‐channel receive coils and three in vivo measurements were analyzed. Reconstruction parameters were systematically varied between R = 2–4, N_ACS = 4–64, b_x = 1–9, and b_y = 2–10 to characterize their influence on image quality and noise. A main aspect of the analysis was to optimize the parameter set with respect to the effectively achieved net image acceleration. Selecting the undersampling factor R as small as possible for a given net acceleration yielded the best result in a clear majority of cases. For all data sets and coil geometries, the optimal kernel sizes and number of ACS lines were similar for a chosen undersampling factor R. In summary, the number of ACS lines should not be chosen below N_ACS = 10–16. A robust choice for the kernel size was b_x = 9 and b_y = 2–4. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

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.     

Parallel and nonparallel simultaneous multislice black-blood double inversion recovery techniques for vessel wall imaging

PURPOSE: To reduce long examination times of black-blood vessel wall imaging by acquiring multiple slices simultaneously and by using parallel acquisition techniques. MATERIALS AND METHODS: DIR-rapid acquisition with relaxation enhancement (RARE) techniques imaging up to 10 simultaneous slices per acquisition with single and multiple 180 degrees -reinversion pulses were developed. A slab-selective reinversion multislice DIR-RARE sequence incorporating generalized autocalibrating partially parallel acquisitions (GRAPPA) imaging was implemented. Four-channel and eight-channel carotid coils were built to test these sequences. A total of 11 subjects were studied. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) efficiency factor (SEF, SNR/unit time/slice) were measured from aortic images of three healthy subjects to determine optimal MR parameters. The DIR-RARE-GRAPPA sequence was run on aortas and carotid arteries of the five remaining healthy subjects and three atherosclerotic patients with optimal parameters (acquisition times 12-21 seconds). RESULTS: SEFs of slab-selective protocols were significantly higher than those of slice-selective protocols, and SEFs of DIR-RARE-GRAPPA protocols were significantly higher than corresponding non-GRAPPA protocols (P < 0.05). CNR was not significantly different for all imaging protocols. The DIR-RARE-GRAPPA multislice sequence showed 8.35-fold time improvement vs. single-slice DIR-2RARE sequence. CONCLUSION: Future MRI atherosclerotic plaque studies can be performed in substantially shorter times using these methods.