def main():
# locate the input data
input_file = existing_filepath(data_path, data_file)
acq_data = AcquisitionData(input_file)
# create reconstruction object
recon = Reconstructor([ \
'NoiseAdjustGadget', \
'PCACoilGadget', \
'CoilReductionGadget(coils_out=16)', \
'gpuRadialSensePrepGadget(' + \
'mode=2,' + \
'profiles_per_frame=16,' + \
'rotations_per_reconstruction=16,' + \
'buffer_frames_per_rotation=16,' + \
'buffer_length_in_rotations=2,' + \
'reconstruction_os_factor_x=1.5,' + \
'reconstruction_os_factor_y=1.5' + \
')', \
'slice0:gpuCgSenseGadget(' + \
'number_of_iterations=10,' + \
'oversampling_factor=1.25,' + \
'output_convergence=true' + \
')', \
'slice1:gpuCgSenseGadget(' + \
'sliceno=1,' + \
'number_of_iterations=10,' + \
'oversampling_factor=1.25,' + \
'output_convergence=true' + \
')', \
'slice2:gpuCgSenseGadget(' + \
'sliceno=2,' + \
'number_of_iterations=10,' + \
'oversampling_factor=1.25,' + \
'output_convergence=true' + \
')', \
'ExtractGadget', 'AutoScaleGadget'])
# provide raw k-space data as input
recon.set_input(acq_data)
# perform reconstruction
recon.process()
# retrieve reconstructed image data
image_data = recon.get_output()
image_data.show(title = 'Images')
def main():
# locate the input data
input_file = existing_filepath(data_path, data_file)
acq_data = AcquisitionData(input_file)
# create reconstruction object
# Rather than using a predefined image reconstruction object, here a new
# image reconstruction object is created by concatinating multiple gadgets
# (for more information on Gadgetron and its gadgets please see:
# https://github.com/gadgetron/.).
# Parameters for individual gadgets can be defined either during the
# creation of the reconstruction object:
# e.g. AcquisitionAccumulateTriggerGadget(trigger_dimension=repetition)
# or by giving a gadget a label (cf. label ex: for the last gadget)
# and using set_gadget_property(label, propery, value).
# The gadgets will be concatenated and will be executed as soon as
# process() is called.
recon = Reconstructor([ \
'NoiseAdjustGadget', \
'AsymmetricEchoAdjustROGadget', \
'RemoveROOversamplingGadget', \
'AcquisitionAccumulateTriggerGadget(trigger_dimension=repetition)', \
'BucketToBufferGadget(split_slices=true, verbose=false)', \
'SimpleReconGadget', 'ImageArraySplitGadget', 'ex:ExtractGadget'])
## 'SimpleReconGadget', 'FatWaterGadget', 'ImageArraySplitGadget', \
## 'PhysioInterpolationGadget', 'ex:ExtractGadget'])
# ExtractGadget defines which type of image should be returned:
# none 0
# magnitude 1
# real 2
# imag 4
# phase 8
# in this example '5' returns both magnitude and imaginary part
## recon.set_gadget_property('ex', 'extract_mask', 5)
recon.set_gadget_property('ex', 'extract_magnitude', True)
recon.set_gadget_property('ex', 'extract_imag', True)
# provide raw k-space data as input
recon.set_input(acq_data)
# perform reconstruction
recon.process()
# retrieve reconstructed image data
image_data = recon.get_output()
# show reconstructed image data
for im in range(image_data.number()):
image = image_data.image(im)
# image types series
# magnitude 1 0
# phase 2 3000
# real 3 1000
# imag 4 2000
im_type = image.image_type()
im_series = image.image_series_index()
print('image: %d, type: %d, series: %d' % (im, im_type, im_series))
image_data.show(title = 'Images magnitude and imaginary part')
if output_file is not None:
# write images to a new group in args.output
# named after the current date and time
time_str = time.asctime()
print('writing to %s' % output_file)
image_data.write(output_file) #, time_str)
def main():
# locate the input data
input_file = existing_filepath(data_path, data_file)
acq_data = AcquisitionData(input_file)
if algorithm == 'SimpleReconGadget':
extra_gadgets = [algorithm]
else:
extra_gadgets = [algorithm, 'GenericReconFieldOfViewAdjustmentGadget']
# create reconstruction object
# Rather than using a predefined image reconstruction object, here a new
# image reconstruction object is created by concatinating multiple gadgets
# (for more information on Gadgetron and its gadgets please see:
# https://github.com/gadgetron/.).
# Parameters for individual gadgets can be defined either during the
# creation of the reconstruction object:
# e.g. AcquisitionAccumulateTriggerGadget(trigger_dimension=repetition)
# or by giving a gadget a label (cf. label ex: for the last gadget)
# and using set_gadget_property(label, propery, value).
# The gadgets will be concatenated and will be executed as soon as
# process() is called.
recon_gadgets = ['NoiseAdjustGadget',
'AsymmetricEchoAdjustROGadget',
'RemoveROOversamplingGadget',
'AcquisitionAccumulateTriggerGadget(trigger_dimension=repetition)',
'BucketToBufferGadget(split_slices=true, verbose=false)'] \
+ extra_gadgets + \
['ImageArraySplitGadget',
'ex:ExtractGadget'
]
recon = Reconstructor(recon_gadgets)
# ExtractGadget defines which type of image should be returned:
# none 0
# magnitude 1
# real 2
# imag 4
# phase 8
# in this example '5' returns both magnitude and imaginary part
## recon.set_gadget_property('ex', 'extract_mask', 5)
# === THE ABOVE IS OBSOLETE, NOW SHOULD USE ===>
if type_to_save == 'mag' or type_to_save == 'all':
recon.set_gadget_property('ex', 'extract_magnitude', True)
if type_to_save == 'imag' or type_to_save == 'all':
recon.set_gadget_property('ex', 'extract_imag', True)
# provide raw k-space data as input
recon.set_input(acq_data)
# optionally set Gadgetron server host and port
recon.set_host('localhost')
# On VM you can try a port other than the default 9002, e.g. 9003, by taking
# the following steps:
# 1) in ~/devel/install/share/gadgetron/config/gadgetron.xml replace
# <port>9002</port> with <port>9003</port>
# 2) go to Settings->Network->Advanced->Port Forwarding and add new rule
# (click on green + in the upper right corner) with Host and Guest ports
# set to 9003
# 3) uncomment the next line
#recon.set_port('9003')
# Note: each gadget chain can run on a different VM - to try, start two VMs
# and do the above steps 1 and 2 on one of them, then add
# recon.set_port('9003') before recon.process in grappa_detail.py
# (where preprocessing will still run on default port 9002).
# perform reconstruction
recon.process()
# retrieve reconstructed image data
image_data = recon.get_output()
# show reconstructed image data
if show_plot:
for im in range(image_data.number()):
image = image_data.image(im)
# image types series
# magnitude 1 0
# phase 2 3000
# real 3 1000
# imag 4 2000
im_type = image.image_type()
im_series = image.image_series_index()
print('image: %d, type: %d, series: %d' % (im, im_type, im_series))
image_data.show(title='Images magnitude and imaginary part')
if output_file is not None:
filename = output_file
i = filename.find('.')
if i < 0:
ext = 'h5'
else:
ext = filename[i + 1:]
filename = filename[:i]
print('writing to %s' % (filename + '.' + ext))
image_data.write(filename, ext=ext)