def grid2D(data, coords, weights, kernel, out_dims, number_threads=8):
# data: np.float32
# coords: np.complex64
# weights: np.float32
# kernel: np.float64
# outdims = [nr_coils, extra_dim2, extra_dim1, mtx_xy, mtx_xy]: int
import bni.gridding.grid_kaiser as bni_grid
[nr_coils, extra_dim2, extra_dim1, mtx_xy, nr_arms, nr_points] = out_dims
# threading is done along the coil dimension. Limit the number of threads to the number of coils:
if number_threads > nr_coils:
number_threads = nr_coils
# make number_threads a divider of nr_coils:
while (nr_coils%number_threads != 0):
number_threads -= 1
# off-center in pixels.
dx = dy = 0.
# gridded kspace
gridded_kspace = np.zeros([nr_coils, extra_dim2, extra_dim1, mtx_xy, mtx_xy], dtype=data.dtype)
# tell the grid routine what shape to produce
outdim = np.array([mtx_xy,mtx_xy,nr_coils], dtype=np.int64)
# coordinate dimensions
if coords.shape[0] == 1:
same_coords_for_all_slices_and_dynamics = True
else:
same_coords_for_all_slices_and_dynamics = False
# grid all slices
dx = dy = 0.
for extra1 in range(extra_dim1):
if same_coords_for_all_slices_and_dynamics:
extra1_coords = 0
else:
extra1_coords = extra1
for extra2 in range(extra_dim2):
#for coil in range(nr_coils):
gridded_kspace[:,extra2,extra1,:,:] = bni_grid.grid(coords[extra1_coords,:,:,:], data[:,extra2,extra1,:,:], weights[extra1_coords,:,:], kernel, outdim, dx, dy, numThreads=number_threads)
return gridded_kspace
def rolloff2D(mtx_xy, kernel, clamp_min_percent=5):
# mtx_xy: int
import numpy as np
import bni.gridding.grid_kaiser as gd
# grid one point at k_0
dx = dy = 0.0
coords = np.array([0,0], dtype='float32')
data = np.array([1.0], dtype='complex64')
weights = np.array([1.0], dtype='float32')
outdim = np.array([mtx_xy, mtx_xy],dtype=np.int64)
# grid -> fft -> |x|
out = np.abs(fft2D(gd.grid(coords, data, weights, kernel, outdim, dx, dy)))
# clamp the lowest values to a percentage of the max
clamp = out.max() * clamp_min_percent/100.0
out[out < clamp] = clamp
# invert
return 1.0/out
def grid2D(data, coords, weights, kernel, out_dims):
# data: np.float32
# coords: np.complex64
# weights: np.float32
# kernel: np.float64
# outdims = [nr_coils, extra_dim2, extra_dim1, mtx_xy, mtx_xy]: int
import bni.gridding.grid_kaiser as bni_grid
[nr_coils, extra_dim2, extra_dim1, mtx_xy, nr_arms, nr_points] = out_dims
# off-center in pixels.
dx = dy = 0.
# gridded kspace
gridded_kspace = np.zeros([nr_coils, extra_dim2, extra_dim1, mtx_xy, mtx_xy], dtype=data.dtype)
# tell the grid routine what shape to produce
outdim = np.array([mtx_xy,mtx_xy], dtype=np.int64)
# coordinate dimensions
if coords.shape[0] == 1:
same_coords_for_all_slices_and_dynamics = True
else:
same_coords_for_all_slices_and_dynamics = False
# grid all slices
dx = dy = 0.
for extra1 in range(extra_dim1):
if same_coords_for_all_slices_and_dynamics:
extra1_coords = 0
else:
extra1_coords = extra1
for extra2 in range(extra_dim2):
for coil in range(nr_coils):
gridded_kspace[coil,extra2,extra1,:,:] = bni_grid.grid(coords[extra1_coords,:,:,:], data[coil,extra2,extra1,:,:], weights[extra1_coords,:,:], kernel, outdim, dx, dy)
return gridded_kspace
