# helpers
fft = lambda x0: np.fft.ifftshift(np.fft.fft2(np.fft.fftshift(
x0, axes=(0, 1)), axes=(0, 1)), axes=(0, 1))
ifft = lambda x0: np.fft.fftshift(np.fft.ifft2(np.fft.ifftshift(
x0, axes=(0, 1)), axes=(0, 1)), axes=(0, 1))
# Sizes we want
N, M, nc = 300, 192, 8
calib_lines = 20
# Make square kspace
assert N >= M, 'First must be largest dim!'
N2 = int(N/2)
imspace = np.flipud(
shepp_logan(N))[..., None]*gaussian_csm(N, N, nc)
# Trim down to make nonsquare
# 1st > 2nd
trim = int((N - M)/2)
pad = int(calib_lines/2)
imspace1 = imspace[:, trim:-trim, :]
kspace1 = fft(imspace1)
calib1 = kspace1[N2-pad:N2+pad, ...].copy()
kspace1[::2, ...] = 0 # Undersample: R=2
# 2nd > 1st
imspace2 = imspace[trim:-trim, ...]
kspace2 = fft(imspace2)
calib2 = kspace2[:, N2-pad:N2+pad, :].copy()
kspace2[:, ::2, :] = 0
'''Basic hp-GRAPPA usage.'''
import numpy as np
from mpl_toolkits.mplot3d import Axes3D # pylint: disable=W0611 # NOQA
import matplotlib.pyplot as plt
from phantominator import shepp_logan
from pygrappa import hpgrappa, mdgrappa
from utils import gaussian_csm
if __name__ == '__main__':
# The much abused Shepp-Logan phantom
N, ncoil = 128, 5
ph = shepp_logan(N)[..., None]*gaussian_csm(N, N, ncoil)
fov = (10e-2, 10e-2) # 10cm x 10cm FOV
# k-space-ify it
ax = (0, 1)
kspace = np.fft.ifftshift(np.fft.fft2(np.fft.fftshift(
ph, axes=ax), axes=ax), axes=ax)
# Get an ACS region
pad = 12
ctr = int(N/2)
calib = kspace[ctr-pad:ctr+pad, ...].copy()
# Undersample: R=3
kspace[0::3, ...] = 0
kspace[1::3, ...] = 0
from time import time
import numpy as np
import matplotlib.pyplot as plt
from phantominator import shepp_logan
from pygrappa import mdgrappa
from utils import gaussian_csm
if __name__ == '__main__':
# Generate fake sensitivity maps: mps
L, M, N = 128, 128, 2
ncoils = 4
mps = gaussian_csm(L, M, ncoils)[..., None, :]
# generate 3D phantom
ph = shepp_logan((L, M, N), zlims=(-.25, .25))
imspace = ph[..., None]*mps
ax = (0, 1, 2)
kspace = np.fft.fftshift(np.fft.fftn(
np.fft.ifftshift(imspace, axes=ax), axes=ax), axes=ax)
# calibrate a kernel
kernel_size = (4, 5, 5)
# undersample by a factor of 2 in both kx and ky
mask = np.ones(kspace.shape, dtype=bool)
mask[::2, 1::2, ...] = False
mask[1::2, ::2, ...] = False
import numpy as np
import matplotlib.pyplot as plt
from phantominator import shepp_logan
from skimage.metrics import normalized_root_mse as compare_nrmse # pylint: disable=E0611,E0401
from tqdm import tqdm
from pygrappa import cgrappa as grappa
from utils import gaussian_csm
if __name__ == '__main__':
# Simple phantom
N = 128
ncoils = 5
csm = gaussian_csm(N, N, ncoils)
ph = shepp_logan(N)[..., None] * csm
# Put into k-space
ax = (0, 1)
kspace = np.fft.ifftshift(np.fft.fft2(np.fft.fftshift(ph, axes=ax),
axes=ax),
axes=ax)
kspace_orig = kspace.copy()
# 20x20 ACS region
pad = 10
ctr = int(N / 2)
calib = kspace[ctr - pad:ctr + pad, ctr - pad:ctr + pad, :].copy()
# R=2x2
'''Basic usage of CG-SENSE implementation.'''
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from phantominator import shepp_logan
from pygrappa import cgsense
from utils import gaussian_csm
if __name__ == '__main__':
N, nc = 128, 4
sens = gaussian_csm(N, N, nc)
im = shepp_logan(N) + np.finfo('float').eps
im = im[..., None] * sens
kspace = np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(im, axes=(0, 1)),
axes=(0, 1)),
axes=(0, 1))
# Undersample
kspace[::2, 1::2, :] = 0
kspace[1::2, ::2, :] = 0
# SOS of the aliased image
aliased = np.fft.ifftshift(np.fft.ifft2(np.fft.fftshift(kspace,
axes=(0, 1)),
axes=(0, 1)),
axes=(0, 1))
aliased = np.sqrt(np.sum(np.abs(aliased)**2, axis=-1))
