def test_stspa_3d_nonexplicit(self):
nz = 3
target, sens = self.problem_3d(3, nz)
dim = target.shape[0]
g, k1, t, s = rf.spiral_arch(0.24, dim, 4e-6, 200, 0.035)
k1 = k1 / dim
k1 = rf.stack_of(k1, nz, 0.1)
A = sp.mri.linop.Sense(sens,
k1,
weights=None,
tseg=None,
ishape=target.shape).H
pulses = sp.mri.rf.stspa(target,
sens,
st=None,
coord=k1,
dt=4e-6,
max_iter=30,
alpha=10,
tol=1E-3,
phase_update_interval=200,
explicit=False)
npt.assert_array_almost_equal(A * pulses, target, 1E-3)
def test_stspa_spiral(self):
target, sens = self.problem_2d(8)
fov = 0.55
gts = 6.4e-6
gslew = 190
gamp = 40
R = 1
dx = 0.025 # in m
# construct a trajectory
g, k, t, s = rf.spiral_arch(fov / R, dx, gts, gslew, gamp)
A = linop.Sense(sens, coord=k, ishape=target.shape).H
pulses = rf.stspa(target,
sens,
k,
dt=4e-6,
alpha=1,
b0=None,
st=None,
explicit=False,
max_iter=100,
tol=1E-4)
npt.assert_array_almost_equal(A * pulses, target, 1E-3)
def test_stspa_3d_explicit(self):
nz = 4
target, sens = self.problem_3d(3, nz)
dim = target.shape[0]
g, k1, t, s = rf.spiral_arch(0.24, dim, 4e-6, 200, 0.035)
k1 = k1 / dim
k1 = rf.stack_of(k1, nz, 0.1)
A = rf.linop.PtxSpatialExplicit(sens,
k1,
dt=4e-6,
img_shape=target.shape,
b0=None)
pulses = sp.mri.rf.stspa(target,
sens,
st=None,
coord=k1,
dt=4e-6,
max_iter=30,
alpha=10,
tol=1E-3,
phase_update_interval=200,
explicit=True)
npt.assert_array_almost_equal(A * pulses, target, 1E-3)
import sigpy.mri as mr import sigpy.mri.rf as rf import numpy as np import sigpy.plot as pl import matplotlib dim = 32 Nc = 8 img_shape = [dim, dim] sens_shape = [Nc, dim, dim] sens = mr.birdcage_maps(sens_shape) pl.ImagePlot(sens) fov = 0.55 # FOV in m N = dim # matrix size gts = 6.4e-6 # hardware dwell time, s gslew = 150 # gradient slew rate in mT/m/ms gamp = 30 # maximum gradient amplitude in mT/m densamp = 10000 # duration of full density sampling (in samples) dentrans = 10000 # duration of transition from low-high density (in samples) R = 1 / 2 # degree of undersampling of outer region of trajectory- let's oversample by a factor of 2 dx = 0.025 # in m rewinder = False # construct a trajectory g, k, t, s = rf.spiral_arch(fov / R, dx, gts, gslew, gamp) #Note that this trajectory is a spiral-out trajectory. #We will simply time-reverse it to create a spiral-in. k = np.flipud(k) g = np.flipud(g)
