# -*- coding: utf-8 -*-
##########################################################################
# pySAP - Copyright (C) CEA, 2017 - 2018
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
"""
Fourier operators for non-Cartesian sampling.
"""
# System import
import warnings
# Package import
from ..base import OperatorBase
from mrinufft import get_operator
[docs]class NonCartesianFFT(OperatorBase):
"""This class wraps around different implementation algorithms for NFFT"""
def __init__(self, samples, shape, implementation='finufft', n_coils=1,
density_comp=None, **kwargs):
""" A small wrapper around mri-nufft package
This is mostly maintained just for legacy reasons (all legacy reconstruction
uses this codes)
Parameters
----------
samples: numpy.ndarray (Mxd)
the samples locations in the Fourier domain where M is the number
of samples and d is the dimensionality of the output data
(2D for an image, 3D for a volume).
shape: tuple of int
shape of the image (not necessarily a square matrix).
implementation: str 'finufft' | 'cufinufft' | 'gpuNUFFT',
default 'finufft'
which implementation of NFFT to use.
n_coils: int default 1
Number of coils used to acquire the signal in case of multiarray
receiver coils acquisition
density_comp: np.ndarray (M,) default None
kwargs: extra keyword args
these arguments are passed to underlying operator. Check the docs of
mrinufft : https://mind-inria.github.io/mri-nufft/
"""
self.shape = shape
self.samples = samples
self.n_coils = n_coils
self.implementation = implementation
self.density_comp = density_comp
self.kwargs = kwargs
self.impl = get_operator(self.implementation)(
self.samples,
self.shape,
density=self.density_comp,
n_coils=self.n_coils,
**self.kwargs
)
[docs] def op(self, data, *args):
"""Compute the masked non-uniform Fourier transform
of an image.
Parameters
----------
img: numpy.ndarray
input N-D array with the same shape as shape.
Returns
-------
masked Fourier transform of the input image.
"""
return self.impl.op(data, *args)
[docs] def adj_op(self, coeffs, *args):
"""Compute the inverse masked non-uniform Fourier
transform of a 1-D coefficients array.
Parameters
----------
x: numpy.ndarray
masked non-uniform Fourier transform 1D data.
Returns
-------
inverse discrete Fourier transform of the input coefficients.
"""
return self.impl.adj_op(coeffs, *args)
