Basis function cross-correlations for Robustk-space sample density compensation, with application to the design of radiofrequency excitations

Free-breathing inner-volume black-blood imaging of the human heart using two-dimensionally selective local excitation at 3 T

Black-blood fast spin-echo imaging is a powerful technique for the evaluation of cardiac anatomy. To avoid fold-over artifacts, using a sufficiently large field of view in phase-encoding direction is mandatory. The related oversampling affects scanning time and respiratory chest motion artifacts are commonly observed. The excitation of a volume that exclusively includes the heart without its surrounding structures may help to improve scan efficiency and minimize motion artifacts. Therefore, and by building on previously reported inner-volume approach, the combination of a black-blood fast spin-echo sequence with a two-dimensionally selective radiofrequency pulse is proposed for selective "local excitation" small field of view imaging of the heart. This local excitation technique has been developed, implemented, and tested in phantoms and in vivo. With this method, small field of view imaging of a user-specified region in the human thorax is feasible, scanning becomes more time efficient, motion artifacts can be minimized, and additional flexibility in the choice of imaging parameters can be exploited.

Convolution kernel design and efficient algorithm for sampling density correction

Sampling density compensation is an important step in non-cartesian image reconstruction. One of the common techniques to determine weights that compensate for differences in sampling density involves a convolution. A new convolution kernel is designed for sampling density attempting to minimize the error in a fully reconstructed image. The resulting weights obtained using this new kernel are compared with various previous methods, showing a reduction in reconstruction error. A computationally efficient algorithm is also presented that facilitates the calculation of the convolution of finite kernels. Both the kernel and the algorithm are extended to 3D.