def compute(self):
import numpy as np
dps = self.getVal('Dims per Set')
mtx_xy = self.getVal('Effective MTX XY')
mtx_z = self.getVal('Effective MTX Z')
numiter = self.getVal('Iterations')
taper = self.getVal('Taper')
kradscale = self.getVal('krad Scale')
crds = self.getData('crds')
inwates = self.getData('wates')
mtxsz_xy = (2.*mtx_xy)+6
mtxsz_z = (2.*mtx_z)+6
sdshape = crds[...,0].shape
maxi = crds.ndim - 2
npts = 1
for i in range(dps):
npts *= sdshape[maxi-i]
nsets = int(crds[...,0].size/npts)
# Reshape crds to be 3 dimensional - # sets, # pts/set, and dimensionality (1-3)
crds = np.reshape(crds.astype(np.float64),(nsets,npts,crds.shape[-1]))
if self.getVal('computenow'):
if inwates is not None:
wates = np.copy(inwates.reshape(nsets,npts).astype(np.float64))
else:
wates = np.ones((nsets,npts), dtype=np.float64)
# import in thread to save namespace
import core.gridding.sdc as sd
for set in range(nsets):
if crds.shape[-1] == 1:
cmtxdim = np.array([mtxsz_xy],dtype=np.int64)
sdcset = sd.oned_sdc(crds[set,:],wates[set,:],cmtxdim,numiter,taper)
if crds.shape[-1] == 2:
cmtxdim = np.array([mtxsz_xy,mtxsz_xy],dtype=np.int64)
sdcset = sd.twod_sdc(crds[set,:],wates[set,:],cmtxdim,numiter,taper)
if crds.shape[-1] == 3:
cmtxdim = np.array([mtxsz_xy,mtxsz_xy,mtxsz_z],dtype=np.int64)
sdcset = sd.threed_sdc(crds[set,:],wates[set,:],cmtxdim,numiter,taper,kradscale)
if set == 0:
sdc = np.expand_dims(sdcset,0)
else:
sdc = np.append(sdc,np.expand_dims(sdcset,0),axis=0)
# Reshape sdc weights to match that of incoming coordinates
self.setData('sdc', np.reshape(sdc,sdshape))
return(0)
def compute(self):
import numpy as np
from scipy import linalg
self.log.node("Virtual Channels node running compute()")
twoD_or_threeD = 2
# GETTING WIDGET INFO
mtx_xy = self.getVal('mtx')
image_ceiling = self.getVal('image ceiling')
crop_left = self.getVal('crop left')
crop_right = self.getVal('crop right')
crop_top = self.getVal('crop top')
crop_bottom = self.getVal('crop bottom')
reset_compute_button = self.getVal('reset compute button each time')
compute = self.getVal('compute')
# number of virtual channels m
m = self.getVal('virtual channels')
numiter = self.getVal('SDC Iterations')
# GETTING PORT INFO
data = self.getData('data').astype(np.complex64, copy=False)
noise = self.getData('noise')
sensitivity_map_uncropped = self.getData('sensitivity map')
param = self.getData('params_in')
# set dimensions
nr_points = data.shape[-1]
nr_arms = data.shape[-2]
nr_coils = data.shape[0]
if data.ndim == 3:
extra_dim1 = 1
extra_dim2 = 1
data.shape = [nr_coils,extra_dim2,extra_dim1,nr_arms,nr_points]
elif data.ndim == 4:
extra_dim1 = data.shape[-3]
extra_dim2 = 1
data.shape = [nr_coils,extra_dim2,extra_dim1,nr_arms,nr_points]
elif data.ndim == 5:
extra_dim1 = data.shape[-3]
extra_dim2 = data.shape[-4]
elif data.ndim > 5:
self.log.warn("Not implemented yet")
print("data shape: " + str(data.shape))
if sensitivity_map_uncropped is None:
# if cropping or image scaling sliders were changed then use the previously stored csm instead of calcluating a new one
has_csm_been_calculated = self.getData('sum of square image')
if ( (has_csm_been_calculated is not None) and
( ('crop left' in self.widgetEvents()) or
('crop right' in self.widgetEvents()) or
('crop top' in self.widgetEvents()) or
('crop bottom' in self.widgetEvents()) or
('image ceiling' in self.widgetEvents()) ) ):
csm = self.getData('masked and normalized sense map')
image = self.getData('sum of square image').copy()
if ( (csm is None) or (image is None) ):
self.log.warn("This should not happen.")
return 1
csm_mtx = csm.shape[-1]
else:
# calculate auto-calibration B1 maps
coords = self.getData('coords').astype(np.float32, copy=False)
if (coords is None):
self.log.warn("Either a sensitiviy map or coords to calculate one is required")
return 1
import bni.gridding.Kaiser2D_utils as kaiser2D
# parameters from UI
UI_width = self.getVal('Autocalibration Width (%)')
UI_taper = self.getVal('Autocalibration Taper (%)')
UI_mask_floor = self.getVal('Mask Floor (% of max mag)')
UI_average_csm = self.getVal('Dynamic data - average all dynamics for csm')
csm_mtx = np.int(0.01 * UI_width * mtx_xy)
if coords.shape[-3]>100:
is_GoldenAngle_data = True
else:
is_GoldenAngle_data = False
if param is not None:
if 'spDYN_GOLDANGLE_ON' in param:
if int(param['spDYN_GOLDANGLE_ON'][0]) == 1:
is_GoldenAngle_data = True
else:
is_GoldenAngle_data = False
print("is GoldenAngle: " + str(is_GoldenAngle_data))
if is_GoldenAngle_data:
nr_arms_cms = self.getVal('# golden angle dynamics for csm')
else:
nr_arms_cms = nr_arms
self.log.debug("nr_arms_cms: " + str(nr_arms_cms))
csm_data = data[...,0:nr_arms_cms,:]
# oversampling: Oversample at the beginning and crop at the end
oversampling_ratio = 2. #1.375
mtx = np.int(csm_mtx * oversampling_ratio)
if mtx%2:
mtx+=1
if oversampling_ratio > 1:
mtx_min = np.int((mtx-csm_mtx)/2)
mtx_max = mtx_min + csm_mtx
else:
mtx_min = 0
mtx_max = mtx
# average dynamics or cardiac phases for csm
if ( (extra_dim1 > 1) and UI_average_csm ):
csm_data = np.sum(csm_data, axis=2)
extra_dim1_csm = 1
csm_data.shape = [nr_coils, extra_dim2, extra_dim1_csm, nr_arms_cms, nr_points]
else:
extra_dim1_csm = extra_dim1
self.log.debug("csm_data shape: " + str(csm_data.shape))
self.log.debug("coords shape: " + str(coords.shape))
# coords dimensions: (add 1 dimension as they could have another dimension for golden angle dynamics
if coords.ndim == 3:
coords.shape = [1,nr_arms,nr_points,twoD_or_threeD]
# create low resolution csm
# cropping the data will make gridding and FFT much faster
magnitude_one_interleave = np.zeros(nr_points)
for x in range(nr_points):
magnitude_one_interleave[x] = np.sqrt( coords[0,0,x,0]**2 + coords[0,0,x,1]**2)
within_csm_width_radius = magnitude_one_interleave[:] < (0.01 * UI_width * 0.5) # for BNI spirals should be 0.45 instead of 0.5
nr_points_csm_width = within_csm_width_radius.sum()
# now set the dimension lists
out_dims_grid = [nr_coils, extra_dim2, extra_dim1_csm, mtx, nr_arms_cms, nr_points_csm_width]
out_dims_fft = [nr_coils, extra_dim2, extra_dim1_csm, mtx, mtx]
csm_data = csm_data[...,0:nr_points_csm_width]
csm_coords = 1. / (0.01 * UI_width) * coords[...,0:nr_arms_cms,0:nr_points_csm_width,:]
# generate SDC based on number of arms and nr of points being used for csm
import core.gridding.sdc as sd
#csm_weights = sd.twod_sdcsp(csm_coords.squeeze().astype(np.float64), numiter, 0.01 * UI_taper, mtx)
cmtxdim = np.array([mtx,mtx],dtype=np.int64)
wates = np.ones((nr_arms_cms * nr_points_csm_width), dtype=np.float64)
coords_for_sdc = csm_coords.astype(np.float64)
coords_for_sdc.shape = [nr_arms_cms * nr_points_csm_width, twoD_or_threeD]
csm_weights = sd.twod_sdc(coords_for_sdc, wates, cmtxdim, numiter, 0.01 * UI_taper )
csm_weights.shape = [1,nr_arms_cms,nr_points_csm_width]
# pre-calculate Kaiser-Bessel kernel
kernel_table_size = 800
kernel = kaiser2D.kaiserbessel_kernel( kernel_table_size, oversampling_ratio)
# pre-calculate the rolloff for the spatial domain
roll = kaiser2D.rolloff2D(mtx, kernel)
# Grid
gridded_kspace = kaiser2D.grid2D(csm_data, csm_coords, csm_weights.astype(np.float32), kernel, out_dims_grid)
self.setData('debug', gridded_kspace)
# filter k-space - not needed anymore as SDC taper is used now.
## win = kaiser2D.window2(gridded_kspace.shape[-2:], windowpct=UI_taper, widthpct=100)
## gridded_kspace *= win
# FFT
image_domain = kaiser2D.fft2D(gridded_kspace, dir=0, out_dims_fft=out_dims_fft)
# rolloff
image_domain *= roll
# crop to original matrix size
csm = image_domain[...,mtx_min:mtx_max,mtx_min:mtx_max]
# normalize by rms (better would be to use a whole body coil image
csm_rms = np.sqrt(np.sum(np.abs(csm)**2, axis=0))
csm = csm / csm_rms
# zero out points that are below mask threshold
thresh = 0.01 * UI_mask_floor * csm_rms.max()
csm *= csm_rms > thresh
# for ROI selection use csm_rms, which still has some contrast
image = csm_rms
image.shape = [csm_mtx,csm_mtx]
image_sos = image.copy()
self.setData('sum of square image', image_sos)
else:
csm = sensitivity_map_uncropped
csm_mtx = csm.shape[-1]
# create sum-of-squares of sensitivity map to allow selection of ROI
image = np.copy(csm)
image = np.sqrt(np.sum(np.abs(image)**2, axis=0))
image.shape = [csm_mtx,csm_mtx]
self.setData('masked and normalized sense map', csm)
# display sum-of-squares of sensitivity map to allow selection of ROI
data_max = image.max()
data_min = image.min()
image[:, crop_left-1] = data_max
image[:, crop_right-1] = data_max
image[crop_top-1, :] = data_max
image[crop_bottom-1, :] = data_max
data_range = data_max - data_min
new_max = data_range * 0.01 * image_ceiling + data_min
dmask = np.ones(image.shape)
image = np.minimum(image,new_max*dmask)
if new_max > data_min:
image = 255.*(image - data_min)/(new_max-data_min)
red = green = blue = np.uint8(image)
alpha = 255. * np.ones(blue.shape)
h, w = red.shape[:2]
image1 = np.zeros((h, w, 4), dtype=np.uint8)
image1[:, :, 0] = red
image1[:, :, 1] = green
image1[:, :, 2] = blue
image1[:, :, 3] = alpha
format_ = QtGui.QImage.Format_RGB32
image2 = QtGui.QImage(image1.data, w, h, format_)
image2.ndarry = image1
self.setAttr('image', val=image2)
# crop sensitivity map
csm.shape = [nr_coils, csm_mtx, csm_mtx]
sensitivity_map = csm[:,crop_top-1:crop_bottom,crop_left-1:crop_right]
#self.setData('debug', np.squeeze(sensitivity_map))
# get sizes
# number of channels n
n = sensitivity_map.shape[-3]
x_size = sensitivity_map.shape[-1]
y_size = sensitivity_map.shape[-2]
nr_pixels = x_size * y_size
if compute:
# noise covariance matrix Psi
noise_cv_matrix = np.cov(noise)
# Cholesky decomposition to determine T, where T Psi T_H = 1
L = np.linalg.cholesky(noise_cv_matrix)
T = np.linalg.inv(L)
# decorrelated sensitivity map S_hat
S_hat = np.zeros([nr_pixels, n], dtype=np.complex64)
for x in range(x_size):
for y in range(y_size):
index = y + x * y_size
S_hat[index, :] = np.dot(T, sensitivity_map[:,y,x])
self.log.debug("after S_hat")
# P = sum of S_hat S_hat_pseudo_inverse over all pixels
P = np.zeros([n,n], dtype=np.complex64)
S_hat_matrix = np.zeros([n,1], dtype=np.complex64)
for index in range(nr_pixels):
# pseudo inverse of S_hat
S_hat_matrix[:,0] = S_hat[index,:]
S_hat_pinv = np.linalg.pinv(S_hat_matrix)
P = P + np.dot(S_hat_matrix, S_hat_pinv)
self.log.debug("after S_hat_pinv")
# singular value decomposition of P
# if P is symmetric and positive definite, the SVD is P = U d U.H instead of P = U d V.H
U, d, V = np.linalg.svd(P)
self.log.debug("after svd")
# the transformation matrix A is then given by A = C U.H T
# C is diagonal matrix with 1 on the first m rows and 0 in the remaining
# instead of using C, only assing mxn to A
C = np.array(np.zeros([n,n]), dtype=np.float32)
self.log.debug("after C")
for x in range(m):
C[x,x]=1.
A_square = np.dot(C, np.dot(U.T.conjugate(), T))
A = A_square[0:m,:]
self.log.debug("after A")
# Compress the data
if data.ndim == 5:
out = np.zeros([m,extra_dim2,extra_dim1,nr_arms,nr_points],dtype=data.dtype)
for extra2 in range(extra_dim2):
for extra1 in range(extra_dim1):
for arm in range(nr_arms):
for point in range(nr_points):
out[:,extra2,extra1,arm,point] = np.dot(A, data[:,extra2,extra1,arm,point])
# SETTING PORT INFO
self.setData('compressed data', np.squeeze(out))
self.setData('A', A)
self.setData('noise covariance', noise_cv_matrix)
# end of compute
if reset_compute_button:
self.setAttr('compute', val=False)
return 0
def compute(self):
import bni.gridding.Kaiser2D_utils as kaiser2D
self.log.debug("Start CG SENSE 2D")
# get port and widget inputs
data = self.getData('data').astype(np.complex64, copy=False)
coords = self.getData('coords').astype(np.float32, copy=False)
weights = self.getData('weights').astype(np.float32, copy=False)
mtx_original = self.getVal('mtx')
iterations = self.getVal('iterations')
step = self.getVal('step')
oversampling_ratio = self.getVal('oversampling ratio')
number_threads = self.getVal('number of threads')
GA = self.getVal('Golden Angle - combine dynamics before gridding')
# for a single iteration step use the csm stored in the out port
if step and (self.getData('oversampled CSM') is not None):
csm = self.getData('oversampled CSM')
else:
csm = self.getData('coil sensitivity')
if csm is not None:
csm = csm.astype(np.complex64, copy=False)
# oversampling: Oversample at the beginning and crop at the end
mtx = np.int(np.around(mtx_original * oversampling_ratio))
if mtx%2:
mtx+=1
if oversampling_ratio > 1:
mtx_min = np.int(np.around((mtx-mtx_original)/2))
mtx_max = mtx_min + mtx_original
else:
mtx_min = 0
mtx_max = mtx
# data dimensions
nr_points = data.shape[-1]
nr_arms = data.shape[-2]
nr_coils = data.shape[0]
if data.ndim == 3:
extra_dim1 = 1
extra_dim2 = 1
data.shape = [nr_coils,extra_dim2,extra_dim1,nr_arms,nr_points]
elif data.ndim == 4:
extra_dim1 = data.shape[-3]
extra_dim2 = 1
data.shape = [nr_coils,extra_dim2,extra_dim1,nr_arms,nr_points]
elif data.ndim == 5:
extra_dim1 = data.shape[-3]
extra_dim2 = data.shape[-4]
elif data.ndim > 5:
self.log.warn("Not implemented yet")
out_dims_grid = [nr_coils, extra_dim2, extra_dim1, mtx, nr_arms, nr_points]
out_dims_degrid = [nr_coils, extra_dim2, extra_dim1, nr_arms, nr_points]
out_dims_fft = [nr_coils, extra_dim2, extra_dim1, mtx, mtx]
iterations_shape = [extra_dim2, extra_dim1, mtx, mtx]
# coords dimensions: (add 1 dimension as they could have another dimension for golden angle dynamics
if coords.ndim == 3:
coords.shape = [1,nr_arms,nr_points,2]
weights.shape = [1,nr_arms,nr_points]
# output including all iterations
x_iterations = np.zeros([iterations,extra_dim2,extra_dim1,mtx_original,mtx_original],dtype=np.complex64)
if step and (iterations > 1):
previous_iterations = self.getData('x iterations')
previous_iterations.shape = [iterations-1,extra_dim2, extra_dim1, mtx_original, mtx_original]
x_iterations[:-1,:,:,:,:] = previous_iterations
# pre-calculate Kaiser-Bessel kernel
self.log.debug("Calculate kernel")
kernel_table_size = 800
kernel = kaiser2D.kaiserbessel_kernel( kernel_table_size, oversampling_ratio)
# pre-calculate the rolloff for the spatial domain
roll = kaiser2D.rolloff2D(mtx, kernel)
# for a single iteration step use the oversampled csm and intermediate results stored in outports
if step and (self.getData('d') is not None):
self.log.debug("Save some time and use the previously determined csm stored in the cropped CSM outport.")
elif GA: #combine data from GA dynamics before gridding, use code from VirtualChannels_GPI.py
# grid images for each phase - needs to be done at some point, not really here for csm though.
self.log.debug("Grid undersampled data")
gridded_kspace = kaiser2D.grid2D(data, coords, weights, kernel, out_dims_grid, number_threads=number_threads)
# FFT
image_domain = kaiser2D.fft2D(gridded_kspace, dir=0, out_dims_fft=out_dims_fft)
# rolloff
image_domain *= roll
twoD_or_threeD = coords.shape[-1]
# parameters from UI
UI_width = self.getVal('Autocalibration Width (%)')
UI_taper = self.getVal('Autocalibration Taper (%)')
UI_mask_floor = self.getVal('Mask Floor (% of max mag)')
mask_dilation = self.getVal('Autocalibration mask dilation [pixels]')
UI_average_csm = self.getVal('Dynamic data - average all dynamics for csm')
numiter = self.getVal('Autocalibration SDC Iterations')
original_csm_mtx = np.int(0.01 * UI_width * mtx_original)
is_GoldenAngle_data = True
nr_arms_csm = self.getVal('# golden angle dynamics for csm')
nr_all_arms_csm = extra_dim1 * nr_arms
extra_dim1_csm = 1
# coords dimensions: (add 1 dimension as they could have another dimension for golden angle dynamics
if coords.ndim == 3:
coords.shape = [1,nr_arms,nr_points,twoD_or_threeD]
# create low resolution csm
# cropping the data will make gridding and FFT much faster
magnitude_one_interleave = np.zeros(nr_points)
for x in range(nr_points):
magnitude_one_interleave[x] = np.sqrt( coords[0,0,x,0]**2 + coords[0,0,x,1]**2)
within_csm_width_radius = magnitude_one_interleave[:] < (0.01 * UI_width * 0.5) # for BNI spirals should be 0.45 instead of 0.5
nr_points_csm_width = within_csm_width_radius.sum()
# in case of radial trajectory, it doesn't start at zero..
found_start_point = 0
found_end_point = 0
for x in range(nr_points):
if ((not found_start_point) and (within_csm_width_radius[x])):
found_start_point = 1
start_point = x
if ((not found_end_point) and (found_start_point) and (not within_csm_width_radius[x])):
found_end_point = 1
end_point = x
if not found_end_point:
end_point = nr_points
self.log.node("Start and end points in interleave are: "+str(start_point)+" and "+str(end_point)+" leading to "+str(nr_points_csm_width)+" points for csm.")
arm_counter = 0
extra_dim1_counter = 0
arm_with_data_counter = 0
while (arm_with_data_counter < nr_arms_csm and extra_dim1_counter < extra_dim1):
if (coords[extra_dim1_counter, arm_counter,0,0] != coords[extra_dim1_counter, arm_counter,-1,0]): #only equal when no data in this interleave during resorting
arm_with_data_counter += 1
arm_counter += 1
if arm_counter == nr_arms:
arm_counter = 0
extra_dim1_counter += 1
self.log.node("Found "+str(arm_with_data_counter)+" arms, and was looking for "+str(nr_arms_csm)+" from a total of "+str(nr_all_arms_csm)+" arms.")
csm_data = np.zeros([nr_coils,extra_dim2,extra_dim1_csm,arm_with_data_counter,nr_points_csm_width], dtype=data.dtype)
csm_coords = np.zeros([1,arm_with_data_counter,nr_points_csm_width,twoD_or_threeD], dtype=coords.dtype)
arm_counter = 0
extra_dim1_counter = 0
arm_with_data_counter = 0
while (arm_with_data_counter < nr_arms_csm and extra_dim1_counter < extra_dim1):
if (coords[extra_dim1_counter, arm_counter,0,0] != coords[extra_dim1_counter, arm_counter,-1,0]): #only equal when no data in this interleave during resorting
csm_data[:,:,0,arm_with_data_counter,:] = data[:,:,extra_dim1_counter,arm_counter,start_point:end_point]
csm_coords[0,arm_with_data_counter,:,:] = coords[extra_dim1_counter,arm_counter,start_point:end_point,:]
arm_with_data_counter += 1
arm_counter += 1
if arm_counter == nr_arms:
arm_counter = 0
extra_dim1_counter += 1
self.log.node("Found "+str(arm_with_data_counter)+" arms, and was looking for "+str(nr_arms_csm)+" from a total of "+str(nr_all_arms_csm)+" arms.")
# now set the dimension lists
out_dims_grid_csm = [nr_coils, extra_dim2, extra_dim1_csm, mtx, arm_with_data_counter, nr_points_csm_width]
out_dims_fft_csm = [nr_coils, extra_dim2, extra_dim1_csm, mtx, mtx]
# generate SDC based on number of arms and nr of points being used for csm
import core.gridding.sdc as sd
#csm_weights = sd.twod_sdcsp(csm_coords.squeeze().astype(np.float64), numiter, 0.01 * UI_taper, mtx)
cmtxdim = np.array([mtx,mtx],dtype=np.int64)
wates = np.ones((arm_with_data_counter * nr_points_csm_width), dtype=np.float64)
coords_for_sdc = csm_coords.astype(np.float64)
coords_for_sdc.shape = [arm_with_data_counter * nr_points_csm_width, twoD_or_threeD]
csm_weights = sd.twod_sdc(coords_for_sdc, wates, cmtxdim, numiter, 0.01 * UI_taper )
csm_weights.shape = [1,arm_with_data_counter,nr_points_csm_width]
# Grid
gridded_kspace_csm = kaiser2D.grid2D(csm_data, csm_coords, csm_weights.astype(np.float32), kernel, out_dims_grid_csm, number_threads=number_threads)
image_domain_csm = kaiser2D.fft2D(gridded_kspace_csm, dir=0, out_dims_fft=out_dims_fft_csm)
# rolloff
image_domain_csm *= roll
# # crop to original matrix size
# csm = image_domain_csm[...,mtx_min:mtx_max,mtx_min:mtx_max]
# normalize by rms (better would be to use a whole body coil image
csm_rms = np.sqrt(np.sum(np.abs(image_domain_csm)**2, axis=0))
image_domain_csm = image_domain_csm / csm_rms
# zero out points that are below mask threshold
thresh = 0.01 * UI_mask_floor * csm_rms.max()
mask = csm_rms > thresh
# use scipy library to grow mask and fill holes.
from scipy import ndimage
mask.shape = [mtx,mtx]
mask = ndimage.morphology.binary_dilation(mask, iterations=mask_dilation)
mask = ndimage.binary_fill_holes(mask)
image_domain_csm *= mask
if extra_dim1 > 1:
csm = np.zeros([nr_coils, extra_dim2, extra_dim1, mtx, mtx], dtype=image_domain_csm.dtype)
for extra_dim1_counter in range(extra_dim1):
csm[:,:,extra_dim1_counter,:,:]=image_domain_csm[:,:,0,:,:]
else:
csm = image_domain_csm
self.setData('oversampled CSM', csm)
self.setData('cropped CSM', csm[...,mtx_min:mtx_max,mtx_min:mtx_max])
else: # this is the normal path (not single iteration step)
# grid to create images that are corrupted by
# aliasing due to undersampling. If the k-space data have an
# auto-calibration region, then this can be used to generate B1 maps.
self.log.debug("Grid undersampled data")
gridded_kspace = kaiser2D.grid2D(data, coords, weights, kernel, out_dims_grid, number_threads=number_threads)
# FFT
image_domain = kaiser2D.fft2D(gridded_kspace, dir=0, out_dims_fft=out_dims_fft)
# rolloff
image_domain *= roll
# calculate auto-calibration B1 maps
if csm is None:
self.log.debug("Generating autocalibrated B1 maps...")
# parameters from UI
UI_width = self.getVal('Autocalibration Width (%)')
UI_taper = self.getVal('Autocalibration Taper (%)')
UI_mask_floor = self.getVal('Mask Floor (% of max mag)')
UI_average_csm = self.getVal('Dynamic data - average all dynamics for csm')
csm = kaiser2D.autocalibrationB1Maps2D(image_domain, taper=UI_taper, width=UI_width, mask_floor=UI_mask_floor, average_csm=UI_average_csm)
else:
# make sure input csm and data are the same mtx size.
# Assuming the FOV was the same: zero-fill in k-space
if csm.ndim != 5:
self.log.debug("Reshape imported csm")
csm.shape = [nr_coils,extra_dim2,extra_dim1,csm.shape[-2],csm.shape[-1]]
if csm.shape[-1] != mtx:
self.log.debug("Interpolate csm to oversampled matrix size")
csm_oversampled_mtx = np.int(csm.shape[-1] * oversampling_ratio)
if csm_oversampled_mtx%2:
csm_oversampled_mtx+=1
out_dims_oversampled_image_domain = [nr_coils, extra_dim2, extra_dim1, csm_oversampled_mtx, csm_oversampled_mtx]
csm = kaiser2D.fft2D(csm, dir=1, out_dims_fft=out_dims_oversampled_image_domain)
csm = kaiser2D.fft2D(csm, dir=0, out_dims_fft=out_dims_fft)
self.setData('oversampled CSM', csm)
self.setData('cropped CSM', csm[...,mtx_min:mtx_max,mtx_min:mtx_max])
# keep a conjugate csm set on hand
csm_conj = np.conj(csm)
## Iteration 1:
if step and (self.getData('d') is not None):
self.log.debug("\tSENSE Iteration: " + str(iterations))
# make sure the loop doesn't start if only one step is needed
iterations = 0
# Get the data from the last execution of this node for an
# additional single iteration.
d = self.getData('d').copy()
r = self.getData('r').copy()
x = self.getData('x').copy()
# A
Ad = csm * d # add coil phase
Ad *= roll # pre-rolloff for degrid convolution
Ad = kaiser2D.fft2D(Ad, dir=1)
Ad = kaiser2D.degrid2D(Ad, coords, kernel, out_dims_degrid, number_threads=number_threads, oversampling_ratio = oversampling_ratio)
Ad = kaiser2D.grid2D(Ad, coords, weights, kernel, out_dims_grid, number_threads=number_threads)
Ad = kaiser2D.fft2D(Ad, dir=0)
Ad *= roll
Ad = csm_conj * Ad # broadcast multiply to remove coil phase
Ad = Ad.sum(axis=0) # assume the coil dim is the first
else:
self.log.debug("\tSENSE Iteration: 1")
# calculate initial conditions
# d_0
d_0 = csm_conj * image_domain # broadcast multiply to remove coil phase
d_0 = d_0.sum(axis=0) # assume the coil dim is the first
# Ad_0:
# degrid -> grid (loop over coils)
Ad_0 = csm * d_0 # add coil phase
Ad_0 *= roll # pre-rolloff for degrid convolution
Ad_0 = kaiser2D.fft2D(Ad_0, dir=1)
Ad_0 = kaiser2D.degrid2D(Ad_0, coords, kernel, out_dims_degrid, number_threads=number_threads, oversampling_ratio = oversampling_ratio)
Ad_0 = kaiser2D.grid2D(Ad_0, coords, weights, kernel, out_dims_grid, number_threads=number_threads)
Ad_0 = kaiser2D.fft2D(Ad_0, dir=0)
Ad_0 *= roll
Ad_0 = csm_conj * Ad_0 # broadcast multiply to remove coil phase
Ad_0 = Ad_0.sum(axis=0) # assume the coil dim is the first
# use the initial conditions for the first iter
r = d = d_0
x = np.zeros_like(d)
Ad = Ad_0
# CG - iter 1 or step
d_last, r_last, x_last = self.do_cg(d, r, x, Ad)
current_iteration = x_last.copy()
current_iteration.shape = iterations_shape
if step:
x_iterations[-1,:,:,:,:] = current_iteration[...,mtx_min:mtx_max,mtx_min:mtx_max]
else:
x_iterations[0,:,:,:,:] = current_iteration[...,mtx_min:mtx_max,mtx_min:mtx_max]
## Iterations >1:
for i in range(iterations-1):
self.log.debug("\tSENSE Iteration: " + str(i+2))
# input the result of the last iter
d = d_last
r = r_last
x = x_last
# A
Ad = csm * d # add coil phase
Ad *= roll # pre-rolloff for degrid convolution
Ad = kaiser2D.fft2D(Ad, dir=1)
Ad = kaiser2D.degrid2D(Ad, coords, kernel, out_dims_degrid, number_threads=number_threads, oversampling_ratio = oversampling_ratio)
Ad = kaiser2D.grid2D(Ad, coords, weights, kernel, out_dims_grid, number_threads=number_threads)
Ad = kaiser2D.fft2D(Ad, dir=0)
Ad *= roll
Ad = csm_conj * Ad # broadcast multiply to remove coil phase
Ad = Ad.sum(axis=0) # assume the coil dim is the first
# CG
d_last, r_last, x_last = self.do_cg(d, r, x, Ad)
current_iteration = x_last.copy()
current_iteration.shape = iterations_shape
x_iterations[i+1,:,:,:,:] = current_iteration[..., mtx_min:mtx_max, mtx_min:mtx_max]
# return the final image
self.setData('d', d_last)
self.setData('r', r_last)
self.setData('x', x_last)
self.setData('out', np.squeeze(current_iteration[..., mtx_min:mtx_max, mtx_min:mtx_max]))
self.setData('x iterations', np.squeeze(x_iterations))
return 0
