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Abstract: The potential advantages of the higher-order resonant modes of a low-pass birdcage coil for parallel imaging are investigated using FDTD simulations in a spherical phantom. Better parallel imaging performance can be achieved in axial planes than in sagittal planes. If more modes are employed, the average g-factor (g mean) and maximum g-factor (g max) will be improved for a specific acceleration factor (R). G-factor performance at 3T (g mean =1.79, g max =3.55) and 7T (g mean =1.60, g max =2.45) can be achieved even with an acceleration factor as high as four when all six order modes of 12-rung low-pass birdcage coil are incorporated for imaging on a mid-axial plane. For a specific number of channels, the optimum combination of corresponding modes can be obtained for different acceleration factors. Based on the g-factor and SNR performance, the total degenerate multi-mode birdcage coil with six order resonant modes has better homogeneous coverage and SENSE performance than the 8-element phased array coil, although requiring fewer channels. In addition, the dielectric effects at high field can improve the parallel imaging performance. [Copyright &y& Elsevier]

Abstract: The generalized auto-calibrating partially parallel acquisition (GRAPPA) is an auto-calibrating parallel imaging technique which incorporates multiple blocks of data to derive the missing signals. In the original GRAPPA reconstruction algorithm only the data points in phase encoding direction are incorporated to reconstruct missing points in k-space. It has been recognized that this scheme can be extended so that data points in readout direction are also utilized and the points are selected based on a k-space locality criterion. In this study, an automatic subset selection strategy is proposed which can provide a tailored selection of source points for reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix of fit, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Also, subset selection in this way has a regularization effect since the vectors corresponding to the smallest singular values are eliminated and consequently the condition of the reconstruction is improved. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can effectively improve SNR and reduce residual artifacts for GRAPPA reconstruction. [Copyright &y& Elsevier]