'''Demo of Non-Cartesian GRAPPA.'''
import numpy as np
import matplotlib.pyplot as plt
from phantominator import radial, kspace_shepp_logan
from pygrappa import ttgrappa
from utils import gridder
if __name__ == '__main__':
# Simulate a radial trajectory
sx, spokes, nc = 128, 128, 8
kx, ky = radial(sx, spokes)
# We reorder the samples like this for easier undersampling later
kx = np.reshape(kx, (sx, spokes)).flatten('F')
ky = np.reshape(ky, (sx, spokes)).flatten('F')
# Sample Shepp-Logan at points (kx, ky) with nc coils:
kspace = kspace_shepp_logan(kx, ky, ncoil=nc)
k = kspace.copy()
# Get some calibration data -- ideally we would want to simulate
# something other than the image we're going to reconstruct, but
# since this is just proof of concept, we'll go ahead
cx = kx.copy()
cy = ky.copy()
calib = k.copy()
# calib = np.tile(calib[:, None, :], (1, 2, 1))
calib = calib[:, None, :] # middle axis is the through-time dim
from pygrappa import grog, radialgrappaop
if __name__ == '__main__':
# Make sure we have the primefac-fork
try:
import primefac # pylint: disable=W0611
except ImportError:
raise ImportError('Need to install fork of primefac: '
'https://github.com/elliptic-shiho/'
'primefac-fork')
# Radially sampled Shepp-Logan
N, spokes, nc = 288, 72, 8
kx, ky = radial(N, spokes)
kx = np.reshape(kx, (N, spokes), 'F').flatten()
ky = np.reshape(ky, (N, spokes), 'F').flatten()
k = kspace_shepp_logan(kx, ky, ncoil=nc)
k = whiten(k) # whitening seems to help conditioning of Gx, Gy
# Put in correct shape for radialgrappaop
k = np.reshape(k, (N, spokes, nc))
kx = np.reshape(kx, (N, spokes))
ky = np.reshape(ky, (N, spokes))
# Get the GRAPPA operators!
t0 = time()
Gx, Gy = radialgrappaop(kx, ky, k)
print('Gx, Gy computed in %g seconds' % (time() - t0))
