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Convolution Kernel Design and Efficient Algorithm for Sampling Density Correction
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DOI：10.1002/mrm.21840.png)
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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 Magn Reson Med 61:439-447, 2009. (c) 2009 Wiley-Liss, Inc.
Keywords:
sampling density
PSF weighting
gridding
non-carteslan
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