Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient approximation of Jacobian matrices involving a non-uniform
  fast Fourier transform (NUFFT)   There is growing interest in learning k-space sampling patterns for MRI using
optimization approaches. For non-Cartesian sampling patterns, reconstruction
methods typically involve non-uniform FFT (NUFFT) operations. A typical NUFFT
method contains frequency domain interpolation using Kaiser-Bessel kernel
values that are retrieved by nearest neighbor look-up in a finely tabulated
kernel. That look-up operation is not differentiable with respect to the
sampling pattern, complicating auto-differentiation routines for
backpropagation (stochastic gradient descent) for sampling pattern
optimization. This paper describes an efficient and accurate approach for
computing approximate gradients with respect to the sampling pattern for
learning k-space sampling. Various numerical experiments validate the accuracy
of the proposed approximation. We also showcase the trajectories optimized for
different iterative reconstruction algorithms, including smooth convex
regularized reconstruction and compressed sensing-based reconstruction.

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The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
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The categories of this paper are: econ
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
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The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys