def reconstruct_noise_scan(filename, noisesetname):
dset = ismrmrd.Dataset(filename,noisesetname)
header = ismrmrd.xsd.CreateFromDocument(dset.read_xml_header())
bw = header.acquisitionSystemInformation.relativeReceiverNoiseBandwidth
# Read the first acquisition to check the number of samples and coils
nacq_noise = dset.number_of_acquisitions()
acq = dset.read_acquisition(0)
nsamp_noise = acq.number_of_samples
ncoils_noise = acq.active_channels
sampletime_noise = acq.sample_time_us
# Accumulate the noise data into a big array
noisedata = np.zeros([ncoils_noise,nsamp_noise,nacq_noise],dtype='complex')
for n in range(nacq_noise):
acq = dset.read_acquisition(n)
noisedata[:,:,n] = acq.data
noisedata = np.reshape(noisedata, [ncoils_noise, nsamp_noise*nacq_noise])
# Calculate the pre-whitening matrix
Mtx = coils.calculate_prewhitening(noisedata, scale_factor=1.0)
# Close the noise data set
dset.close()
return noise_struct(Mtx, bw)
def process(self, acq, data, *args):
if acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):
self.noise_data.append((acq, data))
else:
if len(self.noise_data):
profiles = len(self.noise_data)
channels = self.noise_data[0][1].shape[0]
samples_per_profile = self.noise_data[0][1].shape[1]
noise = np.zeros((channels, profiles * samples_per_profile),
dtype=np.complex64)
counter = 0
for p in self.noise_data:
noise[:, counter *
samples_per_profile:(counter * samples_per_profile +
samples_per_profile)] = p[1]
counter = counter + 1
scale = (acq.sample_time_us /
self.noise_data[0][0].sample_time_us) * 0.79
self.noise_dmtx = coils.calculate_prewhitening(
noise, scale_factor=scale)
#Test the noise adjust
d = self.noise_data[0][1]
d2 = coils.apply_prewhitening(d, self.noise_dmtx)
self.noise_data = list()
if self.noise_dmtx is not None:
data2 = coils.apply_prewhitening(data, self.noise_dmtx)
else:
data2 = data
self.put_next(acq, data2)
return 0
def process(self,acq,data,*args):
if acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):
self.noise_data.append((acq,data))
else:
if len(self.noise_data):
profiles = len(self.noise_data)
channels = self.noise_data[0][1].shape[0]
samples_per_profile = self.noise_data[0][1].shape[1]
noise = np.zeros((channels,profiles*samples_per_profile),dtype=np.complex64)
counter = 0
for p in self.noise_data:
noise[:,counter*samples_per_profile:(counter*samples_per_profile+samples_per_profile)] = p[1]
counter = counter + 1
scale = (acq.sample_time_us/self.noise_data[0][0].sample_time_us)*0.79
self.noise_dmtx = coils.calculate_prewhitening(noise,scale_factor=scale)
#Test the noise adjust
d = self.noise_data[0][1]
d2 = coils.apply_prewhitening(d, self.noise_dmtx)
self.noise_data = list()
if self.noise_dmtx is not None:
data2 = coils.apply_prewhitening(data, self.noise_dmtx)
else:
data2 = data
self.put_next(acq,data2)
return 0
def reconstruct_noise_scan(filename, noisesetname):
dset = ismrmrd.Dataset(filename, noisesetname)
header = ismrmrd.xsd.CreateFromDocument(dset.read_xml_header())
bw = header.acquisitionSystemInformation.relativeReceiverNoiseBandwidth
# Read the first acquisition to check the number of samples and coils
nacq_noise = dset.number_of_acquisitions()
acq = dset.read_acquisition(0)
nsamp_noise = acq.number_of_samples
ncoils_noise = acq.active_channels
sampletime_noise = acq.sample_time_us
# Accumulate the noise data into a big array
noisedata = np.zeros([ncoils_noise, nsamp_noise, nacq_noise],
dtype='complex')
for n in range(nacq_noise):
acq = dset.read_acquisition(n)
noisedata[:, :, n] = acq.data
noisedata = np.reshape(noisedata, [ncoils_noise, nsamp_noise * nacq_noise])
# Calculate the pre-whitening matrix
Mtx = coils.calculate_prewhitening(noisedata, scale_factor=1.0)
# Close the noise data set
dset.close()
return noise_struct(Mtx, bw)
#Undersample
reload(simulation)
acc_factor = 2
ref_lines = 16
(data,pat) = simulation.sample_data(phan,csm,acc_factor,ref_lines)
#%%
#Add noise
noise = np.random.standard_normal(data.shape) + 1j*np.random.standard_normal(data.shape)
noise = (5.0/matrix_size)*noise
kspace = np.logical_or(pat==1,pat==3).astype('float32')*(data + noise)
data = (pat>0).astype('float32')*(data + noise)
#%%
#Calculate the noise prewhitening matrix
dmtx = coils.calculate_prewhitening(noise)
#%%
# Apply prewhitening
kspace = coils.apply_prewhitening(kspace, dmtx)
data = coils.apply_prewhitening(data, dmtx)
#%%
#Reconstruct aliased images
alias_img = transform.transform_kspace_to_image(kspace,dim=(1,2)) * np.sqrt(acc_factor)
show.imshow(abs(alias_img))
#%%
reload(sense)
# TODO: Currently ignoring noise scans
if not acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):
raise Exception(
"Errror: non noise scan found in noise calibration")
noise[:, acqnum * noise_samples:acqnum * noise_samples +
noise_samples] = acq.data
noise = noise.astype('complex64')
#Calculate prewhiterner taking BWs into consideration
a = dset.read_acquisition(firstacq)
data_dwell_time = a.sample_time_us
noise_receiver_bw_ratio = 0.79
dmtx = coils.calculate_prewhitening(
noise,
scale_factor=(data_dwell_time / noise_dwell_time) *
noise_receiver_bw_ratio)
#================================================================================
#
#
#================================================================================
# assemble information
#================================================================================
print "trajectory: ", enc.trajectory
# 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
def compute(self):
do_squeeze = self.getVal('Squeeze')
do_remos = self.getVal('Remove Oversampling')
do_zeropad = self.getVal('Zeropad')
do_noiseadj = self.getVal('Noise Adjust')
receiver_noise_bw = self.getVal('Receiver Noise BW Ratio')
#Get the file name use the file browser widget
fname = gpi.TranslateFileURI(self.getVal('File Browser'))
#Check if the file exists
if not os.path.exists(fname):
self.log.node("Path does not exist: "+str(fname))
return 0
dset = ismrmrd.Dataset(fname, 'dataset', create_if_needed=False)
xml_header = dset.read_xml_header()
header = ismrmrd.xsd.CreateFromDocument(xml_header)
self.setData('ISMRMRDHeader', str(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 != None:
nslices = enc.encodingLimits.slice.maximum + 1
else:
nslices = 1
if enc.encodingLimits.repetition != None:
nreps = enc.encodingLimits.repetition.maximum + 1
else:
nreps = 1
if enc.encodingLimits.contrast != None:
ncontrasts = enc.encodingLimits.contrast.maximum + 1
else:
ncontrasts = 1
# In case there are noise scans in the actual dataset, we will skip them.
noise_data = list()
noise_dmtx = None
firstacq=0
for acqnum in range(dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
if acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):
noise_data.append((acq.getHead(),acq.data))
continue
else:
firstacq = acqnum
break
if len(noise_data):
profiles = len(noise_data)
channels = noise_data[0][1].shape[0]
samples_per_profile = noise_data[0][1].shape[1]
noise = np.zeros((channels,profiles*samples_per_profile),dtype=np.complex64)
counter = 0
for p in noise_data:
noise[:,counter*samples_per_profile:(counter*samples_per_profile+samples_per_profile)] = p[1]
counter = counter + 1
self.setData('noise',noise)
scale = (acq.sample_time_us/noise_data[0][0].sample_time_us)*receiver_noise_bw
noise_dmtx = coils.calculate_prewhitening(noise,scale_factor=scale)
noise_data = list()
# Empty array for the output data
acq = dset.read_acquisition(firstacq)
ro_length = acq.number_of_samples
padded_ro_length = (acq.number_of_samples-acq.center_sample)*2
size_nx = 0
if do_remos:
size_nx = rNx
do_zeropad = True
elif do_zeropad:
size_nx = padded_ro_length
else:
size_nx = ro_length
all_data = np.zeros((nreps, ncontrasts, nslices, ncoils, eNz, eNy, size_nx), dtype=np.complex64)
# Loop through the rest of the acquisitions and stuff
for acqnum in range(firstacq,dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
acq_data_prw = np.zeros(acq.data.shape,dtype=np.complex64)
acq_data_prw[:] = acq.data[:]
if do_noiseadj and (noise_dmtx is not None):
acq_data_prw = coils.apply_prewhitening(acq_data_prw, noise_dmtx)
data2 = None
if (padded_ro_length != ro_length) and do_zeropad: #partial fourier
data2 = np.zeros((acq_data_prw.shape[0], padded_ro_length),dtype=np.complex64)
offset = (padded_ro_length>>1) - acq.center_sample
data2[:,0+offset:offset+ro_length] = acq_data_prw
else:
data2 = acq_data_prw
if do_remos:
data2=transform.transform_kspace_to_image(data2,dim=(1,))
data2=data2[:,(padded_ro_length>>2):(padded_ro_length>>2)+(padded_ro_length>>1)]
data2=transform.transform_image_to_kspace(data2,dim=(1,)) * np.sqrt(float(padded_ro_length)/ro_length)
# Stuff into the buffer
rep = acq.idx.repetition
contrast = acq.idx.contrast
slice = acq.idx.slice
y = acq.idx.kspace_encode_step_1
z = acq.idx.kspace_encode_step_2
all_data[rep, contrast, slice, :, z, y, :] = data2
all_data = all_data.astype('complex64')
if do_squeeze:
all_data = np.squeeze(all_data)
self.setData('data',all_data)
return 0
# Undersample
reload(simulation)
acc_factor = 2
ref_lines = 16
(data, pat) = simulation.sample_data(phan, csm, acc_factor, ref_lines)
#%%
# Add noise
noise = np.random.standard_normal(data.shape) + 1j * np.random.standard_normal(data.shape)
noise = (5.0 / matrix_size) * noise
kspace = np.logical_or(pat == 1, pat == 3).astype("float32") * (data + noise)
data = (pat > 0).astype("float32") * (data + noise)
#%%
# Calculate the noise prewhitening matrix
dmtx = coils.calculate_prewhitening(noise)
#%%
# Apply prewhitening
kspace = coils.apply_prewhitening(kspace, dmtx)
data = coils.apply_prewhitening(data, dmtx)
#%%
# Reconstruct aliased images
alias_img = transform.transform_kspace_to_image(kspace, dim=(1, 2)) * np.sqrt(acc_factor)
show.imshow(abs(alias_img))
#%%
reload(sense)
for acqnum in range(dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
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
#Calculate prewhiterner taking BWs into consideration
a = dset.read_acquisition(firstacq)
data_dwell_time = a.sample_time_us
noise_receiver_bw_ratio = 0.79
dmtx = coils.calculate_prewhitening(noise,scale_factor=(data_dwell_time/noise_dwell_time)*noise_receiver_bw_ratio)
#%%
# Process the actual data
all_data = np.zeros((nreps, ncontrasts, nslices, ncoils, eNz, eNy, rNx), dtype=np.complex64)
# Loop through the rest of the acquisitions and stuff
for acqnum in range(firstacq,dset.number_of_acquisitions()):
acq = dset.read_acquisition(acqnum)
acq_data_prw = coils.apply_prewhitening(acq.data,dmtx)
# Remove oversampling if needed
if eNx != rNx:
xline = transform.transform_kspace_to_image(acq_data_prw, [1])
