Increasing efficiency of parallel imaging for 2D multislice acquisitions

Abstract

Parallel imaging algorithms require precise knowledge about the spatial sensitivity variation of the receiver coils to reconstruct images with full field of view (FOV) from undersampled Fourier encoded data. Sensitivity information must either be given a priori, or estimated from calibration data acquired along with the actual image data. In this study, two approaches are presented, which require very little or no additional data at all for calibration in two-dimensional multislice acquisitions. Instead of additional data, information from spatially adjacent slices is used to estimate coil sensitivity information, thereby increasing the efficiency of parallel imaging. The proposed approaches rely on the assumption that over a small range of slices, coil sensitivities vary smoothly in slice direction. Both methods are implemented as variants of the GRAPPA algorithm. For a given effective acceleration, superior image quality is achieved compared to the conventional GRAPPA method. For the latter calibration lines for coil weight computation must be acquired in addition to the undersampled k-spaces for coil weight computation, thus requiring higher k-space undersampling, that is, a higher reduction factor to achieve the same effective acceleration. The proposed methods are particularly suitable to speed up parallel imaging for clinical applications where the reduction factor is limited to two or three. Magn

Continuously moving table time‐of‐flight angiography of the peripheral veins

“…Possible acquisition strategies may be view sharing techniques (41) or compressed sensing (42). Recently, another promising method for parallel imaging, termed z‐GRAPPA (43), has been introduced, which uses k ‐space information from spatially adjacent 2D axial slices.…”

A kernel approach to parallel MRI reconstruction

“…When the net acceleration factor is high, GRAPPA reconstruction can suffer from aliasing artifacts and noise amplifications. Methods have been developed in recent years to improve GRAPPA using localized coil calibration and variable density sampling [2], multicolumn multiline interpolation [3], regularization [4,5], iteratively reweighted least-squares [6], high-pass filtering [7], cross-validation [8,9], iterative optimization [10], virtual coil using conjugate symmetric signals [11], multi-slice weighting [12], or infinite pulse response (IIR) filtering [13], etc.…”

Improve GRAPPA with Cross-sampled ACS Lines and Nonlinear Kernel Model

“…When the high outer reduction factor (R) is applied for GRAPPA, the reconstruction noises will increase distinctly. Therefore, an amount of reconstruction methods have been proposed to reduce aliasing artifacts and noised for improving image quality, such as, regularization [5], reweighted least squares [6], high-pass filtering [7], cross-validation [8], iterative optimization [9], virtual coil using conjugate symmetric signals [10], multi-slice weighting [11], an infinite impulse response model [12], nonlinear model [13], etc. Only a few methods modify the data acquisition procedure to improve GRAPPA, such as, variable density sampling [14], cross sampling [15], etc.…”