# -*- coding: utf-8 -*- #%% #Basic setup import numpy as np from ismrmrdtools import simulation, coils, show matrix_size = 256 csm = simulation.generate_birdcage_sensitivities(matrix_size) phan = simulation.phantom(matrix_size) coil_images = np.tile(phan, (8, 1, 1)) * csm show.imshow(abs(coil_images), tile_shape=(4, 2)) (csm_est, rho) = coils.calculate_csm_walsh(coil_images) combined_image = np.sum(csm_est * coil_images, axis=0) show.imshow(abs(csm_est), tile_shape=(4, 2), scale=(0, 1)) show.imshow(abs(combined_image), scale=(0, 1))
import numpy as np
import scipy as sp
from ismrmrdtools import sense, grappa, show, simulation, transform,coils
from importlib import reload
#%%
#import some data
exercise_data = sp.io.loadmat('hansen_exercises2.mat')
csm = np.transpose(exercise_data['smaps'])
pat = np.transpose(exercise_data['sp'])
data = np.transpose(exercise_data['data'])
kspace = np.logical_or(pat==1,pat==3).astype('float32')*(data)
acc_factor = 4
alias_img = transform.transform_kspace_to_image(kspace,dim=(1,2)) * np.sqrt(acc_factor)
show.imshow(abs(alias_img))
(unmix_grappa,gmap_grappa) = grappa.calculate_grappa_unmixing(data, acc_factor, data_mask=pat>1, csm=csm,kernel_size=(4,5))
#(unmix_grappa,gmap_grappa) = grappa.calculate_grappa_unmixing(data, acc_factor, data_mask=pat>1)
show.imshow(abs(gmap_grappa),colorbar=True)
recon_grappa = np.squeeze(np.sum(alias_img * unmix_grappa,0))
show.imshow(abs(recon_grappa),colorbar=True)
sp.io.savemat('tmp_data.mat',{'pat_py': pat,'data_py': data,'csm_py': csm,'alias_img_py':alias_img,'unmix_grappa_py':unmix_grappa})
#%%
#Reload some modules
reload(show)
reload(sense)
reload(grappa)
reload(simulation)
from rms_coil_combine import RMSCoilCombine
#%% Setup gadgets
g3 = RMSCoilCombine()
g2 = AccumulateAndRecon(g3)
g1 = Remove2xOversampling(g2)
#%% Load file
filename = 'testdata.h5'
if not os.path.isfile(filename):
print("%s is not a valid file" % filename)
raise SystemExit
dset = ismrmrd.Dataset(filename, 'dataset', create_if_needed=False)
#%% Send in data
#First ISMRMRD XML header
g1.process_config(dset.read_xml_header())
g2.process_config(dset.read_xml_header())
g3.process_config(dset.read_xml_header())
# Loop through the rest of the acquisitions and stuff
for acqnum in range(0,dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
g1.process(acq.getHead(),acq.data)
#%%
#Get result and display
res = g3.get_results()
show.imshow(np.squeeze(abs(res[0][1])))
from rms_coil_combine import RMSCoilCombine
#%% Setup gadgets
g3 = RMSCoilCombine()
g2 = AccumulateAndRecon(g3)
g1 = Remove2xOversampling(g2)
#%% Load file
filename = 'testdata.h5'
if not os.path.isfile(filename):
print("%s is not a valid file" % filename)
raise SystemExit
dset = ismrmrd.Dataset(filename, 'dataset', create_if_needed=False)
#%% Send in data
#First ISMRMRD XML header
g1.process_config(dset.read_xml_header())
g2.process_config(dset.read_xml_header())
g3.process_config(dset.read_xml_header())
# Loop through the rest of the acquisitions and stuff
for acqnum in range(0,dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
g1.process(acq.getHead(),np.transpose(acq.data))
#%%
#Get result and display
res = g3.get_results()
show.imshow(np.squeeze(abs(res[0][1])))
y = acq.idx.kspace_encode_step_1
z = acq.idx.kspace_encode_step_2
all_data[rep, contrast, slice, :, z, y, :] = acq.data
# Reconstruct images
images = np.zeros((nreps, ncontrasts, nslices, eNz, eNy, rNx), dtype=np.float32)
for rep in range(nreps):
for contrast in range(ncontrasts):
for slice in range(nslices):
# FFT
if eNz>1:
#3D
im = transform.transform_kspace_to_image(all_data[rep,contrast,slice,:,:,:,:], [1,2,3])
else:
#2D
im = transform.transform_kspace_to_image(all_data[rep,contrast,slice,:,0,:,:], [1,2])
# Sum of squares
im = np.sqrt(np.sum(np.abs(im) ** 2, 0))
# Stuff into the output
if eNz>1:
#3D
images[rep,contrast,slice,:,:,:] = im
else:
#2D
images[rep,contrast,slice,0,:,:] = im
# Show an image
show.imshow(np.squeeze(images[0,0,0,:,:,:]))
# Reconstruct images
images = np.zeros((nreps, ncontrasts, nslices, eNz, eNy, rNx),
dtype=np.float32)
for rep in range(nreps):
for contrast in range(ncontrasts):
for slice in range(nslices):
# FFT
if eNz > 1:
#3D
im = transform.transform_kspace_to_image(
all_data[rep, contrast, slice, :, :, :, :], [1, 2, 3])
else:
#2D
im = transform.transform_kspace_to_image(
all_data[rep, contrast, slice, :, 0, :, :], [1, 2])
# Sum of squares
im = np.sqrt(np.sum(np.abs(im)**2, 0))
# Stuff into the output
if eNz > 1:
#3D
images[rep, contrast, slice, :, :, :] = im
else:
#2D
images[rep, contrast, slice, 0, :, :] = im
# Show an image
show.imshow(np.squeeze(images[0, 0, 0, :, :, :]))
def test_bssfp_data(self):
'''Sample bSSFP data set.'''
dset = pyport(file=self.sample, debug=False)
import numpy as np
import ismrmrd
import ismrmrd.xsd
from ismrmrdtools import show, transform
header = ismrmrd.xsd.CreateFromDocument(dset.read_xml_header())
enc = header.encoding[0]
# Matrix size
eNx = enc.encodedSpace.matrixSize.x
eNy = enc.encodedSpace.matrixSize.y
eNz = enc.encodedSpace.matrixSize.z
rNx = enc.reconSpace.matrixSize.x
_rNy = enc.reconSpace.matrixSize.y
_rNz = enc.reconSpace.matrixSize.z
# Field of View
_eFOVx = enc.encodedSpace.fieldOfView_mm.x
_eFOVy = enc.encodedSpace.fieldOfView_mm.y
_eFOVz = enc.encodedSpace.fieldOfView_mm.z
_rFOVx = enc.reconSpace.fieldOfView_mm.x
_rFOVy = enc.reconSpace.fieldOfView_mm.y
_rFOVz = enc.reconSpace.fieldOfView_mm.z
# Number of Slices, Reps, Contrasts, etc.
ncoils = header.acquisitionSystemInformation.receiverChannels
if enc.encodingLimits.slice is not None:
nslices = enc.encodingLimits.slice.maximum + 1
else:
nslices = 1
if enc.encodingLimits.average is not None:
nreps = enc.encodingLimits.average.maximum + 1
else:
nreps = 1
if enc.encodingLimits.contrast is not None:
ncontrasts = enc.encodingLimits.contrast.maximum + 1
else:
ncontrasts = 1
# TODO loop through the acquisitions looking for noise scans
firstacq = 0
for acqnum in range(dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
# TODO: Currently ignoring noise scans
if acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):
print("Found noise scan at acq ", acqnum)
continue
else:
firstacq = acqnum
print("Imaging acquisition starts acq ", acqnum)
break
# Initialiaze a storage array
all_data = np.zeros(
(nreps, ncontrasts, nslices, ncoils, eNz, eNy, rNx),
dtype=np.complex64) #pylint: disable=E1101
# Loop through the rest of the acquisitions and stuff
for acqnum in range(firstacq, dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
# TODO: this is where we would apply noise pre-whitening
# Remove oversampling if needed
if eNx != rNx:
xline = transform.transform_kspace_to_image(acq.data, [1])
x0 = int((eNx - rNx) / 2)
x1 = int((eNx - rNx) / 2 + rNx)
xline = xline[:, x0:x1]
acq.resize(rNx, acq.active_channels, acq.trajectory_dimensions) #pylint: disable=E1101
acq.center_sample = int(rNx / 2)
# need to use the [:] notation here to fill the data
acq.data[:] = transform.transform_image_to_kspace(xline, [1])
# Stuff into the buffer
rep = acq.idx.average #pylint: disable=E1101
contrast = acq.idx.contrast #pylint: disable=E1101
slise = acq.idx.slice #pylint: disable=E1101
y = acq.idx.kspace_encode_step_1 #pylint: disable=E1101
z = acq.idx.kspace_encode_step_2 #pylint: disable=E1101
all_data[rep, contrast, slise, :, z, y, :] = acq.data
# Reconstruct images
images = np.zeros((nreps, ncontrasts, nslices, eNz, eNy, rNx),
dtype=np.float32) #pylint: disable=E1101
for rep in range(nreps):
for contrast in range(ncontrasts):
for slise in range(nslices):
# FFT
if eNz > 1:
#3D
im = transform.transform_kspace_to_image(
all_data[rep, contrast, slise, ...], [1, 2, 3])
else:
#2D
im = transform.transform_kspace_to_image(
all_data[rep, contrast, slise, :, 0, ...], [1, 2])
# Sum of squares
im = np.sqrt(np.sum(np.abs(im)**2, 0))
# Stuff into the output
if eNz > 1:
#3D
images[rep, contrast, slise, ...] = im
else:
#2D
images[rep, contrast, slise, 0, ...] = im
# Show an image
show.imshow(np.squeeze(images[0, 0, 0, ...]))
all_data = all_data.astype('complex64')
#%%
# Coil combination
coil_images = transform.transform_kspace_to_image(
np.squeeze(np.mean(all_data, 0)), (1, 2))
(csm, rho) = coils.calculate_csm_walsh(coil_images)
csm_ss = np.sum(csm * np.conj(csm), 0)
csm_ss = csm_ss + 1.0 * (csm_ss < np.spacing(1)).astype('float32')
if acc_factor > 1:
coil_data = np.squeeze(np.mean(all_data, 0))
reload(grappa)
(unmix, gmap) = grappa.calculate_grappa_unmixing(coil_data, acc_factor)
#(unmix,gmap) = sense.calculate_sense_unmixing(acc_factor,csm)
show.imshow(abs(gmap), colorbar=True, scale=(1, 2))
recon = np.zeros((nreps, ncontrasts, nslices, eNz, eNy, rNx),
dtype=np.complex64)
for r in range(0, nreps):
recon_data = transform.transform_kspace_to_image(
np.squeeze(all_data[r, :, :, :, :, :, :]),
(1, 2)) * np.sqrt(acc_factor)
if acc_factor > 1:
recon[r, :, :, :, :] = np.sum(unmix * recon_data, 0)
else:
recon[r, :, :, :, :] = np.sum(np.conj(csm) * recon_data, 0)
show.imshow(np.squeeze(np.std(np.abs(recon), 0)), colorbar=True, scale=(1, 2))
# -*- coding: utf-8 -*-
#%%
#Basic setup
import time
import numpy as np
from ismrmrdtools import simulation, coils, show
matrix_size = 256
csm = simulation.generate_birdcage_sensitivities(matrix_size)
phan = simulation.phantom(matrix_size)
coil_images = phan[np.newaxis, :, :] * csm
show.imshow(abs(coil_images), tile_shape=(4, 2))
tstart = time.time()
(csm_est, rho) = coils.calculate_csm_walsh(coil_images)
print("Walsh coil estimation duration: {}s".format(time.time() - tstart))
combined_image = np.sum(csm_est * coil_images, axis=0)
show.imshow(abs(csm_est), tile_shape=(4, 2), scale=(0, 1))
show.imshow(abs(combined_image), scale=(0, 1))
tstart = time.time()
(csm_est2, rho2) = coils.calculate_csm_inati_iter(coil_images)
print("Inati coil estimation duration: {}s".format(time.time() - tstart))
combined_image2 = np.sum(csm_est2 * coil_images, axis=0)
show.imshow(abs(csm_est2), tile_shape=(4, 2), scale=(0, 1))
show.imshow(abs(combined_image2), scale=(0, 1))
# Basic setup
import numpy as np
import scipy as sp
from ismrmrdtools import sense, grappa, show, simulation, transform, coils
#%%
# import some data
exercise_data = sp.io.loadmat("hansen_exercises2.mat")
csm = np.transpose(exercise_data["smaps"])
pat = np.transpose(exercise_data["sp"])
data = np.transpose(exercise_data["data"])
kspace = np.logical_or(pat == 1, pat == 3).astype("float32") * (data)
acc_factor = 4
alias_img = transform.transform_kspace_to_image(kspace, dim=(1, 2)) * np.sqrt(acc_factor)
show.imshow(abs(alias_img))
(unmix_grappa, gmap_grappa) = grappa.calculate_grappa_unmixing(
data, acc_factor, data_mask=pat > 1, csm=csm, kernel_size=(4, 5)
)
# (unmix_grappa,gmap_grappa) = grappa.calculate_grappa_unmixing(data, acc_factor, data_mask=pat>1)
show.imshow(abs(gmap_grappa), colorbar=True)
recon_grappa = np.squeeze(np.sum(alias_img * unmix_grappa, 0))
show.imshow(abs(recon_grappa), colorbar=True)
sp.io.savemat(
"tmp_data.mat",
{"pat_py": pat, "data_py": data, "csm_py": csm, "alias_img_py": alias_img, "unmix_grappa_py": unmix_grappa},
)
#%%
y = acq.idx.kspace_encode_step_1
z = acq.idx.kspace_encode_step_2
all_data[rep, contrast, slice, :, z, y, :] = acq.data
all_data = all_data.astype('complex64')
#%%
# Coil combination
coil_images = transform.transform_kspace_to_image(np.squeeze(np.mean(all_data,0)),(1,2))
(csm,rho) = coils.calculate_csm_walsh(coil_images)
csm_ss = np.sum(csm * np.conj(csm),0)
csm_ss = csm_ss + 1.0*(csm_ss < np.spacing(1)).astype('float32')
if acc_factor > 1:
coil_data = np.squeeze(np.mean(all_data,0))
reload(grappa)
(unmix,gmap) = grappa.calculate_grappa_unmixing(coil_data, acc_factor)
#(unmix,gmap) = sense.calculate_sense_unmixing(acc_factor,csm)
show.imshow(abs(gmap),colorbar=True,scale=(1,2))
recon = np.zeros((nreps, ncontrasts, nslices, eNz, eNy, rNx), dtype=np.complex64)
for r in range(0,nreps):
recon_data = transform.transform_kspace_to_image(np.squeeze(all_data[r,:,:,:,:,:,:]),(1,2))*np.sqrt(acc_factor)
if acc_factor > 1:
recon[r,:,:,:,:] = np.sum(unmix * recon_data,0)
else:
recon[r,:,:,:,:] = np.sum(np.conj(csm) * recon_data,0)
show.imshow(np.squeeze(np.std(np.abs(recon),0)),colorbar=True,scale=(1,2))
