  Multi-dimensional non-Cartesian MRI encoding using the precomputed
interpolator can encounter the curse of dimensionality, in which the
interpolator size exceeds the available memory on the parallel accelerators.
Here we reformulate the multi-dimensional non-uniform fast Fourier transform
(NUFFT) to a tensor form. The exponentially growing size of the fully
precomputed interpolator can be reduced by tensor analysis. We propose a
tree-like, mixed-radix tensor method which flexibly reduces the storage of the
NUFFT. A parallel tensor product algorithm is proposed and tested with in vivo
cardiac MRI data. Cross-architecture comparisons show that up to 88.1% and
62.4% memory savings are seen in 3D and 2D CINE MRI, respectively, subject only
to a negligible loss of accuracy compared to the double-precision CPU version.
