Reconstruction methods

NUFFT

Note

Currently, only one single reconstruction method exists here - despite more (like NUFFT or GRAPPA) being used in many scripts since a long time. We could integrate them here with MR-zero.

Nufft reconstruction is not integrated into mr0 directly, but can be realized with torchkbnufft and code similar to the following:

import torchkbnufft as tkbn

# Construct a sequence "seq" and simulate it, resulting in "signal"

kdata = signal.view(1, 1, -1)
ktraj = seq.get_kspace()[:, :2].T / (2 * np.pi)
im_size = (64, 64)

adjnufft_ob = tkbn.KbNufftAdjoint(im_size)
dcomp = tkbn.calc_density_compensation_function(ktraj, im_size=im_size)
reco = adjnufft_ob(kdata * dcomp, ktraj).view(im_size)

reco_adjoint

Adjoint reconstruction builds a very simple backwards version of the encoding / measurement operation. It essentially resembles a DFT and is thus slower and consumes more memory than an FFT, but can handle any readout trajectory.